Jul 25, 2025
DeepSeek Proves Reinforcement Learning Alone Can Achieve Advanced Reasoning Without Supervision
Conor Bronsdon
Head of Developer Awareness
Conor Bronsdon
Head of Developer Awareness
You've probably assumed that matching the reasoning capabilities of proprietary giants demands mountains of supervised data. DeepSeek's training methods and model architecture analysis prove otherwise.
Recently, the DeepSeek team unveiled two openly licensed models—DeepSeek-R1-Zero and DeepSeek-R1—that rival OpenAI's o1 while leaning almost entirely on reinforcement learning.
The headline numbers stop you in your tracks: 79.8% on AIME and 97.3% on MATH-500, scores that place the models shoulder-to-shoulder with OpenAI-o1-1217 on formal math and STEM reasoning benchmarks.
What makes this feat remarkable is how R1-Zero reached this level. Starting from a generic base model, it climbed from 15.6% to 71% accuracy on AIME with nothing but large-scale RL. The model developed spontaneous "aha moments" where it rewrites its own solution path mid-stream.
By open-sourcing the full pipeline and weights, DeepSeek challenges the dogma that you must start with supervised fine-tuning to teach large language models to reason.
Explore the Research Paper: DeepSeek-R1 (Incentivizing Reasoning Capability in LLMs via Reinforcement Learning)
We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies
Summary: Two-Model Approach Revolutionizes LLM Reasoning Training
You no longer need mountains of supervised data to teach an LLM how to reason. This DeepSeek's research compares two distinct model paths:
DeepSeek-R1-Zero, which relies purely on reinforcement learning
DeepSeek-R1 that layers a brief supervised "cold-start" on top of RL to smooth the rough edges.
By placing the models side-by-side, the team exposes both the promise and the pitfalls of pure RL—remarkable leaps in problem-solving skill, yet clumsy language—while showing how a small dose of curated examples fixes usability without dulling the capability.
Model #1: DeepSeek-R1-Zero (Pure Reinforcement Learning Approach)
Training reasoning models without supervised fine-tuning seemed impossible until R1-Zero proved otherwise. The model begins life as a base model and jumps straight into large-scale RL.
Early in training, it barely clears 15% accuracy on the AIME math benchmark. After repeated policy updates with the GRPO algorithm and simple rule-based rewards for correctness and output format, it rockets to roughly 71%—a four-fold improvement.
Along the way, you see emergent behaviors that nobody explicitly programmed: self-verification steps, reflective "let me rethink" moments, and sprawling chains of thought that probe alternate solution paths.
These discoveries validate that clear reward signals alone can nurture reasoning capabilities. The model's raw output feels jagged—language mixing, cryptic notation, and little concern for readability. Those quirks trace back to its single-minded optimization for accuracy, proving that pure RL can unlock capability but leaves user experience to chance.
Model #2: DeepSeek-R1 (Multi-Stage Training for Production Readiness)
When you need clean answers alongside sharp reasoning, the hybrid R1 shows the path forward. On this model, they first collect a small batch of expertly written, long chain-of-thought exemplars and run a short supervised fine-tune, just enough to teach the model structured markdown, consistent language, and human-friendly pacing.
They then resume RL, reuse the same rule-based rewards, and add a language-consistency bonus to stamp out multilingual drift.
A rejection-sampling pass curates 800k high-quality traces for a brief SFT refresher before a final round of RL across all tasks. The payoff is striking: R1 reaches about 79% on AIME—on par with OpenAI's o1—while delivering neatly segmented "Reasoning" and "Answer" blocks that you can drop straight into an app.
A sprinkle of supervised structure turns the raw RL discovery into a model your users can actually enjoy.
DeepSeek’s Five Training Stages and Technical Framework
DeepSeek's methodology walks you through a five-step pipeline that turns a raw base model into a polished reasoning assistant. Each stage adds specific capabilities while fixing flaws uncovered in earlier phases, relying on GRPO with rule-based rewards rather than neural critics.
Stage #1: Cold-Start Data Collection and Foundation
Before you ever reach reinforcement learning, the team primes the model with a compact but dense set of chain-of-thought exemplars. They harvest thousands of long, well-structured solutions by combining few-shot prompting, expert-written answers, and lightly cleaned outputs from the earlier R1-Zero run.
Each example follows a strict template—reasoning steps in markdown, followed by a short summary—so the model immediately learns how to think out loud without rambling.
This seed corpus does more than improve readability. It stabilizes later RL by giving the model a coherent starting syntax and discouraging the multilingual code-switching that plagued R1-Zero.
You only need a few thousand high-quality traces instead of millions of labeled pairs, keeping data costs low while delivering faster convergence once RL begins. Think of it as teaching the apprentice to organize their notebook before throwing them into real problem-solving.
Stage #2: Reasoning-Oriented Reinforcement Learning
With a disciplined writing habit in place, you can push the model into heavy reasoning territory. GRPO targets math, coding, and science tasks where correctness is easy to verify automatically.
Rewards come in two flavors: numerical accuracy and adherence to the markdown template. A smaller bonus discourages language mixing, nudging the model to stick to a single tongue throughout an answer.
The rule-based reward system—"did the equation pass?"—skips the instability of neural reward models and allows quick iteration. During this phase, the model invents surprisingly human work habits: it re-derives formulas to double-check itself, flags uncertain steps, and shortens explanations once confidence is high.
Those emergent behaviors happen because the reward focuses on the outcome, not the style. The model discovers whatever strategies raise its score. By the end of Stage 2, you have a specialist who demolishes math benchmarks but still lacks breadth.
Stage #3: Rejection Sampling and Data Curation
Now the specialist becomes a teacher. A frozen checkpoint generates millions of candidate answers. Automated filters keep about 600k reasoning traces and 200k general responses that meet strict readability and correctness thresholds. Any output with tangled language, missing markdown, or incoherent logic gets tossed.
This self-generated corpus widens coverage beyond rule-friendly domains. Prompts about writing, factual QA, and open-ended analysis are mixed in, ensuring the next training stage won't forget everyday conversational skills.
The two-model loop—one model producing, another model critiquing—means you upgrade quality without human raters hovering over every sample. For you, that translates to rapid dataset growth controlled by code, not contractors, while keeping noisy data at bay.
Stage #4: Multi-Domain Supervised Fine-Tuning
Fine-tuning is next in line. It ingests the curated 800k-sample mix over two swift epochs, absorbing the rigorous reasoning patterns without losing fluency in everyday tasks. The corpus blends 75% STEM explanations with 25% prose, so the model maintains numerical precision while picking up stylistic versatility.
This supervised pass also smooths over RL artifacts—odd variable names, terse proofs—so your users receive answers that read naturally.
The fine-tune neither erodes the math gains from Stage 2 nor the broader knowledge added in Stage 3, showing that a modest SFT interlude can harmonize capabilities instead of overriding them. By the end, you hold a balanced generalist who still reasons step by step.
Stage #5: Final Reinforcement Learning for All Scenarios
The closing act reintroduces RL, but now across every scenario you care about. Rule-based rewards still score math and code, while lightweight neural critics judge helpfulness, safety, and brevity for open-ended prompts. The summary section of each answer carries extra weight: it must be correct, concise, and safe, even if the hidden reasoning chain is longer.
Parallel objectives occasionally collide—conciseness can clash with transparency—so the reward function decays template penalties once the model consistently respects the format.
This frees capacity to optimize clarity instead. Continuous evaluation on held-out benchmarks guards against regressions, and temperature-controlled pass@k scoring flags brittle edge cases early.
When this stage finishes, you have an assistant who solves competition math, writes coherent essays, and refuses unsafe requests, all with the same transparent reasoning style that defined their training journey.
Practical Takeaways
Large-scale reinforcement learning isn't just a research curiosity—it changes how you can build, refine, and ship language models. DeepSeek's open methods surface six lessons you can apply immediately, even if your computing budget is modest.
The research demonstrates that:
Pure RL Eliminates the Supervised Data Bottleneck: The R1-Zero experiment lifted AIME accuracy from 15.6 to 71 percent without a single supervised example. When labels are scarce, you can explore data-light pipelines that still deliver strong reasoning capabilities.
Rule-Based Rewards Beat Neural Alternatives for Stability: DeepSeek scored exact math and code answers rather than relying on subjective human ratings. This approach sidesteps the instability common in neural reward models while simplifying your training pipeline.
Cold-Start Data Injection Accelerates Everything: A brief infusion of high-quality chain-of-thought samples cleans up language mixing and gives you readable outputs from the first RL epoch. Your convergence speeds up dramatically compared to pure RL from scratch.
Staged Pipelines Balance Capability with Usability: You don't have to choose between raw performance and production-ready alignment. DeepSeek's approach—cold start, reasoning RL, data curation, SFT, final RL—delivers both frontier performance and practical deployment readiness.
Distillation Trumps Direct Small-Model Training: Rather than running RL on smaller models directly, transfer knowledge from larger reasoning specialists. This approach delivered 7B-parameter models that eclipse GPTs on math while fitting everyday hardware.
Self-Verification Emerges Naturally during RL Training: Your models will develop reflection capabilities without explicit programming. Monitor pass@k with non-zero temperature to surface these behaviors—it provides more reliable performance signals than single-shot accuracy.
These insights invite systematic experimentation, and the open-weight release means you can start adapting them to your own workflows today.
Final Thoughts
DeepSeek's research upends the idea that you need mountains of labeled data to teach an LLM how to reason. The pure reinforcement-learning run—R1-Zero—jumped from 15.6% to 71% accuracy on AIME, then reached 79.8% with their hybrid pipeline to rival OpenAI-o1.
This proves that carefully designed rewards can substitute for human annotations at scale. The open-sourced checkpoints let you verify every decision, trace chain-of-thought outputs, and distill the method into smaller models that outperform direct training on the same compute budget.
The emergent behaviors the authors highlight deserve your attention—self-verification, reflection, and those uncanny "aha" moments where the model rewrites its own solution mid-stream. These patterns hint at a future where reinforcement learning, not supervised fine-tuning, drives the biggest leaps in reasoning performance.
These contributions democratize advanced reasoning, making it feasible for any team with moderate GPUs to build transparent, efficient, and continually improving language models.
However, reproducing that level of DeepSeek's sophistication in production requires careful tracking of every training stage, reward adjustment, and emergent behavior. You need advanced monitoring platforms like Galileo, which support the evaluation and development approaches demonstrated in this DeepSeek research.
Explore how Galileo can help you implement the reasoning development approaches pioneered in this DeepSeek research.
You've probably assumed that matching the reasoning capabilities of proprietary giants demands mountains of supervised data. DeepSeek's training methods and model architecture analysis prove otherwise.
Recently, the DeepSeek team unveiled two openly licensed models—DeepSeek-R1-Zero and DeepSeek-R1—that rival OpenAI's o1 while leaning almost entirely on reinforcement learning.
The headline numbers stop you in your tracks: 79.8% on AIME and 97.3% on MATH-500, scores that place the models shoulder-to-shoulder with OpenAI-o1-1217 on formal math and STEM reasoning benchmarks.
What makes this feat remarkable is how R1-Zero reached this level. Starting from a generic base model, it climbed from 15.6% to 71% accuracy on AIME with nothing but large-scale RL. The model developed spontaneous "aha moments" where it rewrites its own solution path mid-stream.
By open-sourcing the full pipeline and weights, DeepSeek challenges the dogma that you must start with supervised fine-tuning to teach large language models to reason.
Explore the Research Paper: DeepSeek-R1 (Incentivizing Reasoning Capability in LLMs via Reinforcement Learning)
We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies
Summary: Two-Model Approach Revolutionizes LLM Reasoning Training
You no longer need mountains of supervised data to teach an LLM how to reason. This DeepSeek's research compares two distinct model paths:
DeepSeek-R1-Zero, which relies purely on reinforcement learning
DeepSeek-R1 that layers a brief supervised "cold-start" on top of RL to smooth the rough edges.
By placing the models side-by-side, the team exposes both the promise and the pitfalls of pure RL—remarkable leaps in problem-solving skill, yet clumsy language—while showing how a small dose of curated examples fixes usability without dulling the capability.
Model #1: DeepSeek-R1-Zero (Pure Reinforcement Learning Approach)
Training reasoning models without supervised fine-tuning seemed impossible until R1-Zero proved otherwise. The model begins life as a base model and jumps straight into large-scale RL.
Early in training, it barely clears 15% accuracy on the AIME math benchmark. After repeated policy updates with the GRPO algorithm and simple rule-based rewards for correctness and output format, it rockets to roughly 71%—a four-fold improvement.
Along the way, you see emergent behaviors that nobody explicitly programmed: self-verification steps, reflective "let me rethink" moments, and sprawling chains of thought that probe alternate solution paths.
These discoveries validate that clear reward signals alone can nurture reasoning capabilities. The model's raw output feels jagged—language mixing, cryptic notation, and little concern for readability. Those quirks trace back to its single-minded optimization for accuracy, proving that pure RL can unlock capability but leaves user experience to chance.
Model #2: DeepSeek-R1 (Multi-Stage Training for Production Readiness)
When you need clean answers alongside sharp reasoning, the hybrid R1 shows the path forward. On this model, they first collect a small batch of expertly written, long chain-of-thought exemplars and run a short supervised fine-tune, just enough to teach the model structured markdown, consistent language, and human-friendly pacing.
They then resume RL, reuse the same rule-based rewards, and add a language-consistency bonus to stamp out multilingual drift.
A rejection-sampling pass curates 800k high-quality traces for a brief SFT refresher before a final round of RL across all tasks. The payoff is striking: R1 reaches about 79% on AIME—on par with OpenAI's o1—while delivering neatly segmented "Reasoning" and "Answer" blocks that you can drop straight into an app.
A sprinkle of supervised structure turns the raw RL discovery into a model your users can actually enjoy.
DeepSeek’s Five Training Stages and Technical Framework
DeepSeek's methodology walks you through a five-step pipeline that turns a raw base model into a polished reasoning assistant. Each stage adds specific capabilities while fixing flaws uncovered in earlier phases, relying on GRPO with rule-based rewards rather than neural critics.
Stage #1: Cold-Start Data Collection and Foundation
Before you ever reach reinforcement learning, the team primes the model with a compact but dense set of chain-of-thought exemplars. They harvest thousands of long, well-structured solutions by combining few-shot prompting, expert-written answers, and lightly cleaned outputs from the earlier R1-Zero run.
Each example follows a strict template—reasoning steps in markdown, followed by a short summary—so the model immediately learns how to think out loud without rambling.
This seed corpus does more than improve readability. It stabilizes later RL by giving the model a coherent starting syntax and discouraging the multilingual code-switching that plagued R1-Zero.
You only need a few thousand high-quality traces instead of millions of labeled pairs, keeping data costs low while delivering faster convergence once RL begins. Think of it as teaching the apprentice to organize their notebook before throwing them into real problem-solving.
Stage #2: Reasoning-Oriented Reinforcement Learning
With a disciplined writing habit in place, you can push the model into heavy reasoning territory. GRPO targets math, coding, and science tasks where correctness is easy to verify automatically.
Rewards come in two flavors: numerical accuracy and adherence to the markdown template. A smaller bonus discourages language mixing, nudging the model to stick to a single tongue throughout an answer.
The rule-based reward system—"did the equation pass?"—skips the instability of neural reward models and allows quick iteration. During this phase, the model invents surprisingly human work habits: it re-derives formulas to double-check itself, flags uncertain steps, and shortens explanations once confidence is high.
Those emergent behaviors happen because the reward focuses on the outcome, not the style. The model discovers whatever strategies raise its score. By the end of Stage 2, you have a specialist who demolishes math benchmarks but still lacks breadth.
Stage #3: Rejection Sampling and Data Curation
Now the specialist becomes a teacher. A frozen checkpoint generates millions of candidate answers. Automated filters keep about 600k reasoning traces and 200k general responses that meet strict readability and correctness thresholds. Any output with tangled language, missing markdown, or incoherent logic gets tossed.
This self-generated corpus widens coverage beyond rule-friendly domains. Prompts about writing, factual QA, and open-ended analysis are mixed in, ensuring the next training stage won't forget everyday conversational skills.
The two-model loop—one model producing, another model critiquing—means you upgrade quality without human raters hovering over every sample. For you, that translates to rapid dataset growth controlled by code, not contractors, while keeping noisy data at bay.
Stage #4: Multi-Domain Supervised Fine-Tuning
Fine-tuning is next in line. It ingests the curated 800k-sample mix over two swift epochs, absorbing the rigorous reasoning patterns without losing fluency in everyday tasks. The corpus blends 75% STEM explanations with 25% prose, so the model maintains numerical precision while picking up stylistic versatility.
This supervised pass also smooths over RL artifacts—odd variable names, terse proofs—so your users receive answers that read naturally.
The fine-tune neither erodes the math gains from Stage 2 nor the broader knowledge added in Stage 3, showing that a modest SFT interlude can harmonize capabilities instead of overriding them. By the end, you hold a balanced generalist who still reasons step by step.
Stage #5: Final Reinforcement Learning for All Scenarios
The closing act reintroduces RL, but now across every scenario you care about. Rule-based rewards still score math and code, while lightweight neural critics judge helpfulness, safety, and brevity for open-ended prompts. The summary section of each answer carries extra weight: it must be correct, concise, and safe, even if the hidden reasoning chain is longer.
Parallel objectives occasionally collide—conciseness can clash with transparency—so the reward function decays template penalties once the model consistently respects the format.
This frees capacity to optimize clarity instead. Continuous evaluation on held-out benchmarks guards against regressions, and temperature-controlled pass@k scoring flags brittle edge cases early.
When this stage finishes, you have an assistant who solves competition math, writes coherent essays, and refuses unsafe requests, all with the same transparent reasoning style that defined their training journey.
Practical Takeaways
Large-scale reinforcement learning isn't just a research curiosity—it changes how you can build, refine, and ship language models. DeepSeek's open methods surface six lessons you can apply immediately, even if your computing budget is modest.
The research demonstrates that:
Pure RL Eliminates the Supervised Data Bottleneck: The R1-Zero experiment lifted AIME accuracy from 15.6 to 71 percent without a single supervised example. When labels are scarce, you can explore data-light pipelines that still deliver strong reasoning capabilities.
Rule-Based Rewards Beat Neural Alternatives for Stability: DeepSeek scored exact math and code answers rather than relying on subjective human ratings. This approach sidesteps the instability common in neural reward models while simplifying your training pipeline.
Cold-Start Data Injection Accelerates Everything: A brief infusion of high-quality chain-of-thought samples cleans up language mixing and gives you readable outputs from the first RL epoch. Your convergence speeds up dramatically compared to pure RL from scratch.
Staged Pipelines Balance Capability with Usability: You don't have to choose between raw performance and production-ready alignment. DeepSeek's approach—cold start, reasoning RL, data curation, SFT, final RL—delivers both frontier performance and practical deployment readiness.
Distillation Trumps Direct Small-Model Training: Rather than running RL on smaller models directly, transfer knowledge from larger reasoning specialists. This approach delivered 7B-parameter models that eclipse GPTs on math while fitting everyday hardware.
Self-Verification Emerges Naturally during RL Training: Your models will develop reflection capabilities without explicit programming. Monitor pass@k with non-zero temperature to surface these behaviors—it provides more reliable performance signals than single-shot accuracy.
These insights invite systematic experimentation, and the open-weight release means you can start adapting them to your own workflows today.
Final Thoughts
DeepSeek's research upends the idea that you need mountains of labeled data to teach an LLM how to reason. The pure reinforcement-learning run—R1-Zero—jumped from 15.6% to 71% accuracy on AIME, then reached 79.8% with their hybrid pipeline to rival OpenAI-o1.
This proves that carefully designed rewards can substitute for human annotations at scale. The open-sourced checkpoints let you verify every decision, trace chain-of-thought outputs, and distill the method into smaller models that outperform direct training on the same compute budget.
The emergent behaviors the authors highlight deserve your attention—self-verification, reflection, and those uncanny "aha" moments where the model rewrites its own solution mid-stream. These patterns hint at a future where reinforcement learning, not supervised fine-tuning, drives the biggest leaps in reasoning performance.
These contributions democratize advanced reasoning, making it feasible for any team with moderate GPUs to build transparent, efficient, and continually improving language models.
However, reproducing that level of DeepSeek's sophistication in production requires careful tracking of every training stage, reward adjustment, and emergent behavior. You need advanced monitoring platforms like Galileo, which support the evaluation and development approaches demonstrated in this DeepSeek research.
Explore how Galileo can help you implement the reasoning development approaches pioneered in this DeepSeek research.
You've probably assumed that matching the reasoning capabilities of proprietary giants demands mountains of supervised data. DeepSeek's training methods and model architecture analysis prove otherwise.
Recently, the DeepSeek team unveiled two openly licensed models—DeepSeek-R1-Zero and DeepSeek-R1—that rival OpenAI's o1 while leaning almost entirely on reinforcement learning.
The headline numbers stop you in your tracks: 79.8% on AIME and 97.3% on MATH-500, scores that place the models shoulder-to-shoulder with OpenAI-o1-1217 on formal math and STEM reasoning benchmarks.
What makes this feat remarkable is how R1-Zero reached this level. Starting from a generic base model, it climbed from 15.6% to 71% accuracy on AIME with nothing but large-scale RL. The model developed spontaneous "aha moments" where it rewrites its own solution path mid-stream.
By open-sourcing the full pipeline and weights, DeepSeek challenges the dogma that you must start with supervised fine-tuning to teach large language models to reason.
Explore the Research Paper: DeepSeek-R1 (Incentivizing Reasoning Capability in LLMs via Reinforcement Learning)
We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies
Summary: Two-Model Approach Revolutionizes LLM Reasoning Training
You no longer need mountains of supervised data to teach an LLM how to reason. This DeepSeek's research compares two distinct model paths:
DeepSeek-R1-Zero, which relies purely on reinforcement learning
DeepSeek-R1 that layers a brief supervised "cold-start" on top of RL to smooth the rough edges.
By placing the models side-by-side, the team exposes both the promise and the pitfalls of pure RL—remarkable leaps in problem-solving skill, yet clumsy language—while showing how a small dose of curated examples fixes usability without dulling the capability.
Model #1: DeepSeek-R1-Zero (Pure Reinforcement Learning Approach)
Training reasoning models without supervised fine-tuning seemed impossible until R1-Zero proved otherwise. The model begins life as a base model and jumps straight into large-scale RL.
Early in training, it barely clears 15% accuracy on the AIME math benchmark. After repeated policy updates with the GRPO algorithm and simple rule-based rewards for correctness and output format, it rockets to roughly 71%—a four-fold improvement.
Along the way, you see emergent behaviors that nobody explicitly programmed: self-verification steps, reflective "let me rethink" moments, and sprawling chains of thought that probe alternate solution paths.
These discoveries validate that clear reward signals alone can nurture reasoning capabilities. The model's raw output feels jagged—language mixing, cryptic notation, and little concern for readability. Those quirks trace back to its single-minded optimization for accuracy, proving that pure RL can unlock capability but leaves user experience to chance.
Model #2: DeepSeek-R1 (Multi-Stage Training for Production Readiness)
When you need clean answers alongside sharp reasoning, the hybrid R1 shows the path forward. On this model, they first collect a small batch of expertly written, long chain-of-thought exemplars and run a short supervised fine-tune, just enough to teach the model structured markdown, consistent language, and human-friendly pacing.
They then resume RL, reuse the same rule-based rewards, and add a language-consistency bonus to stamp out multilingual drift.
A rejection-sampling pass curates 800k high-quality traces for a brief SFT refresher before a final round of RL across all tasks. The payoff is striking: R1 reaches about 79% on AIME—on par with OpenAI's o1—while delivering neatly segmented "Reasoning" and "Answer" blocks that you can drop straight into an app.
A sprinkle of supervised structure turns the raw RL discovery into a model your users can actually enjoy.
DeepSeek’s Five Training Stages and Technical Framework
DeepSeek's methodology walks you through a five-step pipeline that turns a raw base model into a polished reasoning assistant. Each stage adds specific capabilities while fixing flaws uncovered in earlier phases, relying on GRPO with rule-based rewards rather than neural critics.
Stage #1: Cold-Start Data Collection and Foundation
Before you ever reach reinforcement learning, the team primes the model with a compact but dense set of chain-of-thought exemplars. They harvest thousands of long, well-structured solutions by combining few-shot prompting, expert-written answers, and lightly cleaned outputs from the earlier R1-Zero run.
Each example follows a strict template—reasoning steps in markdown, followed by a short summary—so the model immediately learns how to think out loud without rambling.
This seed corpus does more than improve readability. It stabilizes later RL by giving the model a coherent starting syntax and discouraging the multilingual code-switching that plagued R1-Zero.
You only need a few thousand high-quality traces instead of millions of labeled pairs, keeping data costs low while delivering faster convergence once RL begins. Think of it as teaching the apprentice to organize their notebook before throwing them into real problem-solving.
Stage #2: Reasoning-Oriented Reinforcement Learning
With a disciplined writing habit in place, you can push the model into heavy reasoning territory. GRPO targets math, coding, and science tasks where correctness is easy to verify automatically.
Rewards come in two flavors: numerical accuracy and adherence to the markdown template. A smaller bonus discourages language mixing, nudging the model to stick to a single tongue throughout an answer.
The rule-based reward system—"did the equation pass?"—skips the instability of neural reward models and allows quick iteration. During this phase, the model invents surprisingly human work habits: it re-derives formulas to double-check itself, flags uncertain steps, and shortens explanations once confidence is high.
Those emergent behaviors happen because the reward focuses on the outcome, not the style. The model discovers whatever strategies raise its score. By the end of Stage 2, you have a specialist who demolishes math benchmarks but still lacks breadth.
Stage #3: Rejection Sampling and Data Curation
Now the specialist becomes a teacher. A frozen checkpoint generates millions of candidate answers. Automated filters keep about 600k reasoning traces and 200k general responses that meet strict readability and correctness thresholds. Any output with tangled language, missing markdown, or incoherent logic gets tossed.
This self-generated corpus widens coverage beyond rule-friendly domains. Prompts about writing, factual QA, and open-ended analysis are mixed in, ensuring the next training stage won't forget everyday conversational skills.
The two-model loop—one model producing, another model critiquing—means you upgrade quality without human raters hovering over every sample. For you, that translates to rapid dataset growth controlled by code, not contractors, while keeping noisy data at bay.
Stage #4: Multi-Domain Supervised Fine-Tuning
Fine-tuning is next in line. It ingests the curated 800k-sample mix over two swift epochs, absorbing the rigorous reasoning patterns without losing fluency in everyday tasks. The corpus blends 75% STEM explanations with 25% prose, so the model maintains numerical precision while picking up stylistic versatility.
This supervised pass also smooths over RL artifacts—odd variable names, terse proofs—so your users receive answers that read naturally.
The fine-tune neither erodes the math gains from Stage 2 nor the broader knowledge added in Stage 3, showing that a modest SFT interlude can harmonize capabilities instead of overriding them. By the end, you hold a balanced generalist who still reasons step by step.
Stage #5: Final Reinforcement Learning for All Scenarios
The closing act reintroduces RL, but now across every scenario you care about. Rule-based rewards still score math and code, while lightweight neural critics judge helpfulness, safety, and brevity for open-ended prompts. The summary section of each answer carries extra weight: it must be correct, concise, and safe, even if the hidden reasoning chain is longer.
Parallel objectives occasionally collide—conciseness can clash with transparency—so the reward function decays template penalties once the model consistently respects the format.
This frees capacity to optimize clarity instead. Continuous evaluation on held-out benchmarks guards against regressions, and temperature-controlled pass@k scoring flags brittle edge cases early.
When this stage finishes, you have an assistant who solves competition math, writes coherent essays, and refuses unsafe requests, all with the same transparent reasoning style that defined their training journey.
Practical Takeaways
Large-scale reinforcement learning isn't just a research curiosity—it changes how you can build, refine, and ship language models. DeepSeek's open methods surface six lessons you can apply immediately, even if your computing budget is modest.
The research demonstrates that:
Pure RL Eliminates the Supervised Data Bottleneck: The R1-Zero experiment lifted AIME accuracy from 15.6 to 71 percent without a single supervised example. When labels are scarce, you can explore data-light pipelines that still deliver strong reasoning capabilities.
Rule-Based Rewards Beat Neural Alternatives for Stability: DeepSeek scored exact math and code answers rather than relying on subjective human ratings. This approach sidesteps the instability common in neural reward models while simplifying your training pipeline.
Cold-Start Data Injection Accelerates Everything: A brief infusion of high-quality chain-of-thought samples cleans up language mixing and gives you readable outputs from the first RL epoch. Your convergence speeds up dramatically compared to pure RL from scratch.
Staged Pipelines Balance Capability with Usability: You don't have to choose between raw performance and production-ready alignment. DeepSeek's approach—cold start, reasoning RL, data curation, SFT, final RL—delivers both frontier performance and practical deployment readiness.
Distillation Trumps Direct Small-Model Training: Rather than running RL on smaller models directly, transfer knowledge from larger reasoning specialists. This approach delivered 7B-parameter models that eclipse GPTs on math while fitting everyday hardware.
Self-Verification Emerges Naturally during RL Training: Your models will develop reflection capabilities without explicit programming. Monitor pass@k with non-zero temperature to surface these behaviors—it provides more reliable performance signals than single-shot accuracy.
These insights invite systematic experimentation, and the open-weight release means you can start adapting them to your own workflows today.
Final Thoughts
DeepSeek's research upends the idea that you need mountains of labeled data to teach an LLM how to reason. The pure reinforcement-learning run—R1-Zero—jumped from 15.6% to 71% accuracy on AIME, then reached 79.8% with their hybrid pipeline to rival OpenAI-o1.
This proves that carefully designed rewards can substitute for human annotations at scale. The open-sourced checkpoints let you verify every decision, trace chain-of-thought outputs, and distill the method into smaller models that outperform direct training on the same compute budget.
The emergent behaviors the authors highlight deserve your attention—self-verification, reflection, and those uncanny "aha" moments where the model rewrites its own solution mid-stream. These patterns hint at a future where reinforcement learning, not supervised fine-tuning, drives the biggest leaps in reasoning performance.
These contributions democratize advanced reasoning, making it feasible for any team with moderate GPUs to build transparent, efficient, and continually improving language models.
However, reproducing that level of DeepSeek's sophistication in production requires careful tracking of every training stage, reward adjustment, and emergent behavior. You need advanced monitoring platforms like Galileo, which support the evaluation and development approaches demonstrated in this DeepSeek research.
Explore how Galileo can help you implement the reasoning development approaches pioneered in this DeepSeek research.
You've probably assumed that matching the reasoning capabilities of proprietary giants demands mountains of supervised data. DeepSeek's training methods and model architecture analysis prove otherwise.
Recently, the DeepSeek team unveiled two openly licensed models—DeepSeek-R1-Zero and DeepSeek-R1—that rival OpenAI's o1 while leaning almost entirely on reinforcement learning.
The headline numbers stop you in your tracks: 79.8% on AIME and 97.3% on MATH-500, scores that place the models shoulder-to-shoulder with OpenAI-o1-1217 on formal math and STEM reasoning benchmarks.
What makes this feat remarkable is how R1-Zero reached this level. Starting from a generic base model, it climbed from 15.6% to 71% accuracy on AIME with nothing but large-scale RL. The model developed spontaneous "aha moments" where it rewrites its own solution path mid-stream.
By open-sourcing the full pipeline and weights, DeepSeek challenges the dogma that you must start with supervised fine-tuning to teach large language models to reason.
Explore the Research Paper: DeepSeek-R1 (Incentivizing Reasoning Capability in LLMs via Reinforcement Learning)
We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies
Summary: Two-Model Approach Revolutionizes LLM Reasoning Training
You no longer need mountains of supervised data to teach an LLM how to reason. This DeepSeek's research compares two distinct model paths:
DeepSeek-R1-Zero, which relies purely on reinforcement learning
DeepSeek-R1 that layers a brief supervised "cold-start" on top of RL to smooth the rough edges.
By placing the models side-by-side, the team exposes both the promise and the pitfalls of pure RL—remarkable leaps in problem-solving skill, yet clumsy language—while showing how a small dose of curated examples fixes usability without dulling the capability.
Model #1: DeepSeek-R1-Zero (Pure Reinforcement Learning Approach)
Training reasoning models without supervised fine-tuning seemed impossible until R1-Zero proved otherwise. The model begins life as a base model and jumps straight into large-scale RL.
Early in training, it barely clears 15% accuracy on the AIME math benchmark. After repeated policy updates with the GRPO algorithm and simple rule-based rewards for correctness and output format, it rockets to roughly 71%—a four-fold improvement.
Along the way, you see emergent behaviors that nobody explicitly programmed: self-verification steps, reflective "let me rethink" moments, and sprawling chains of thought that probe alternate solution paths.
These discoveries validate that clear reward signals alone can nurture reasoning capabilities. The model's raw output feels jagged—language mixing, cryptic notation, and little concern for readability. Those quirks trace back to its single-minded optimization for accuracy, proving that pure RL can unlock capability but leaves user experience to chance.
Model #2: DeepSeek-R1 (Multi-Stage Training for Production Readiness)
When you need clean answers alongside sharp reasoning, the hybrid R1 shows the path forward. On this model, they first collect a small batch of expertly written, long chain-of-thought exemplars and run a short supervised fine-tune, just enough to teach the model structured markdown, consistent language, and human-friendly pacing.
They then resume RL, reuse the same rule-based rewards, and add a language-consistency bonus to stamp out multilingual drift.
A rejection-sampling pass curates 800k high-quality traces for a brief SFT refresher before a final round of RL across all tasks. The payoff is striking: R1 reaches about 79% on AIME—on par with OpenAI's o1—while delivering neatly segmented "Reasoning" and "Answer" blocks that you can drop straight into an app.
A sprinkle of supervised structure turns the raw RL discovery into a model your users can actually enjoy.
DeepSeek’s Five Training Stages and Technical Framework
DeepSeek's methodology walks you through a five-step pipeline that turns a raw base model into a polished reasoning assistant. Each stage adds specific capabilities while fixing flaws uncovered in earlier phases, relying on GRPO with rule-based rewards rather than neural critics.
Stage #1: Cold-Start Data Collection and Foundation
Before you ever reach reinforcement learning, the team primes the model with a compact but dense set of chain-of-thought exemplars. They harvest thousands of long, well-structured solutions by combining few-shot prompting, expert-written answers, and lightly cleaned outputs from the earlier R1-Zero run.
Each example follows a strict template—reasoning steps in markdown, followed by a short summary—so the model immediately learns how to think out loud without rambling.
This seed corpus does more than improve readability. It stabilizes later RL by giving the model a coherent starting syntax and discouraging the multilingual code-switching that plagued R1-Zero.
You only need a few thousand high-quality traces instead of millions of labeled pairs, keeping data costs low while delivering faster convergence once RL begins. Think of it as teaching the apprentice to organize their notebook before throwing them into real problem-solving.
Stage #2: Reasoning-Oriented Reinforcement Learning
With a disciplined writing habit in place, you can push the model into heavy reasoning territory. GRPO targets math, coding, and science tasks where correctness is easy to verify automatically.
Rewards come in two flavors: numerical accuracy and adherence to the markdown template. A smaller bonus discourages language mixing, nudging the model to stick to a single tongue throughout an answer.
The rule-based reward system—"did the equation pass?"—skips the instability of neural reward models and allows quick iteration. During this phase, the model invents surprisingly human work habits: it re-derives formulas to double-check itself, flags uncertain steps, and shortens explanations once confidence is high.
Those emergent behaviors happen because the reward focuses on the outcome, not the style. The model discovers whatever strategies raise its score. By the end of Stage 2, you have a specialist who demolishes math benchmarks but still lacks breadth.
Stage #3: Rejection Sampling and Data Curation
Now the specialist becomes a teacher. A frozen checkpoint generates millions of candidate answers. Automated filters keep about 600k reasoning traces and 200k general responses that meet strict readability and correctness thresholds. Any output with tangled language, missing markdown, or incoherent logic gets tossed.
This self-generated corpus widens coverage beyond rule-friendly domains. Prompts about writing, factual QA, and open-ended analysis are mixed in, ensuring the next training stage won't forget everyday conversational skills.
The two-model loop—one model producing, another model critiquing—means you upgrade quality without human raters hovering over every sample. For you, that translates to rapid dataset growth controlled by code, not contractors, while keeping noisy data at bay.
Stage #4: Multi-Domain Supervised Fine-Tuning
Fine-tuning is next in line. It ingests the curated 800k-sample mix over two swift epochs, absorbing the rigorous reasoning patterns without losing fluency in everyday tasks. The corpus blends 75% STEM explanations with 25% prose, so the model maintains numerical precision while picking up stylistic versatility.
This supervised pass also smooths over RL artifacts—odd variable names, terse proofs—so your users receive answers that read naturally.
The fine-tune neither erodes the math gains from Stage 2 nor the broader knowledge added in Stage 3, showing that a modest SFT interlude can harmonize capabilities instead of overriding them. By the end, you hold a balanced generalist who still reasons step by step.
Stage #5: Final Reinforcement Learning for All Scenarios
The closing act reintroduces RL, but now across every scenario you care about. Rule-based rewards still score math and code, while lightweight neural critics judge helpfulness, safety, and brevity for open-ended prompts. The summary section of each answer carries extra weight: it must be correct, concise, and safe, even if the hidden reasoning chain is longer.
Parallel objectives occasionally collide—conciseness can clash with transparency—so the reward function decays template penalties once the model consistently respects the format.
This frees capacity to optimize clarity instead. Continuous evaluation on held-out benchmarks guards against regressions, and temperature-controlled pass@k scoring flags brittle edge cases early.
When this stage finishes, you have an assistant who solves competition math, writes coherent essays, and refuses unsafe requests, all with the same transparent reasoning style that defined their training journey.
Practical Takeaways
Large-scale reinforcement learning isn't just a research curiosity—it changes how you can build, refine, and ship language models. DeepSeek's open methods surface six lessons you can apply immediately, even if your computing budget is modest.
The research demonstrates that:
Pure RL Eliminates the Supervised Data Bottleneck: The R1-Zero experiment lifted AIME accuracy from 15.6 to 71 percent without a single supervised example. When labels are scarce, you can explore data-light pipelines that still deliver strong reasoning capabilities.
Rule-Based Rewards Beat Neural Alternatives for Stability: DeepSeek scored exact math and code answers rather than relying on subjective human ratings. This approach sidesteps the instability common in neural reward models while simplifying your training pipeline.
Cold-Start Data Injection Accelerates Everything: A brief infusion of high-quality chain-of-thought samples cleans up language mixing and gives you readable outputs from the first RL epoch. Your convergence speeds up dramatically compared to pure RL from scratch.
Staged Pipelines Balance Capability with Usability: You don't have to choose between raw performance and production-ready alignment. DeepSeek's approach—cold start, reasoning RL, data curation, SFT, final RL—delivers both frontier performance and practical deployment readiness.
Distillation Trumps Direct Small-Model Training: Rather than running RL on smaller models directly, transfer knowledge from larger reasoning specialists. This approach delivered 7B-parameter models that eclipse GPTs on math while fitting everyday hardware.
Self-Verification Emerges Naturally during RL Training: Your models will develop reflection capabilities without explicit programming. Monitor pass@k with non-zero temperature to surface these behaviors—it provides more reliable performance signals than single-shot accuracy.
These insights invite systematic experimentation, and the open-weight release means you can start adapting them to your own workflows today.
Final Thoughts
DeepSeek's research upends the idea that you need mountains of labeled data to teach an LLM how to reason. The pure reinforcement-learning run—R1-Zero—jumped from 15.6% to 71% accuracy on AIME, then reached 79.8% with their hybrid pipeline to rival OpenAI-o1.
This proves that carefully designed rewards can substitute for human annotations at scale. The open-sourced checkpoints let you verify every decision, trace chain-of-thought outputs, and distill the method into smaller models that outperform direct training on the same compute budget.
The emergent behaviors the authors highlight deserve your attention—self-verification, reflection, and those uncanny "aha" moments where the model rewrites its own solution mid-stream. These patterns hint at a future where reinforcement learning, not supervised fine-tuning, drives the biggest leaps in reasoning performance.
These contributions democratize advanced reasoning, making it feasible for any team with moderate GPUs to build transparent, efficient, and continually improving language models.
However, reproducing that level of DeepSeek's sophistication in production requires careful tracking of every training stage, reward adjustment, and emergent behavior. You need advanced monitoring platforms like Galileo, which support the evaluation and development approaches demonstrated in this DeepSeek research.
Explore how Galileo can help you implement the reasoning development approaches pioneered in this DeepSeek research.
Conor Bronsdon
Conor Bronsdon
Conor Bronsdon
Conor Bronsdon