Build A Reasoning Model From Scratch 7: Reinforcement Learning 2 (Tracking, KL Terms, And More)
This advanced tutorial deep dives into optimizing the Group Relative Policy Optimization (GRPO) algorithm for training reasoning models. Key improvements covered include implementing clipped policy ratios to stabilize training updates, incorporating a KL divergence loss term to constrain model deviation from a reference model, and utilizing specialized format rewards (e.g., think tags) to guide model output structure. The session also provides detailed analysis of advanced training metrics—such as advantage statistics and entropy—and discusses best practices for running long-term, resource-intensive training jobs.
Key takeaways
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Stabilizing Training with Clipped Policy Ratios
1:33:38
To prevent large, unstable updates (overshooting) during training, the GRPO algorithm can be enhanced by implementing clipped policy ratios. This constrains the size of the policy updates, leading to a more stable training process.
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Advanced Metrics for Diagnosis
32:09
Beyond standard loss and reward average, tracking metrics like advantage statistics (mean near zero, high standard deviation) and entropy (measuring model uncertainty) provides deeper insight into the training signal and model behavior.
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Implementing Format Rewards
20:30
A format reward can be added to the total reward function (Total Reward = Correctness Reward + Format Reward) to encourage the model to adhere to specific output structures, such as using 'think tags' for reasoning steps.
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KL Divergence for Model Constraint
18:32
The KL loss term explicitly limits how much the current model's policy can deviate from a reference model (e.g., the initial model). While powerful, the speaker notes it can be overly complex and is not always necessary, especially in math-based reasoning tasks.