AI BriefingRethinking How to Solve Hard Problems Using a Replay Buffer
Key point
ZPPO uses a Replay Buffer to let the model repeatedly learn from difficult questions, improving its ability to solve hard problems without degrading generalization performance.
Details
The existing Distillation approach has the problem that it makes the model simply imitate the teacher's answers, causing overfitting to the dataset and reducing generalization ability. On the other hand, RL methods such as GRPO help the model reason autonomously and thus preserve generalization performance, but they have the limitation that Hard Questions with an accuracy close to 0 get discarded during training, causing the model to miss the opportunity to learn from them.
To address this, the proposed ZPPO introduces a Replay Buffer that stores difficult questions with an accuracy below 50% and lets the model repeatedly encounter them again. In this process, BCQ (Best Correct Question) and NCQ (Negative Correct Question) prompts are generated using the teacher's correct answers and the student's incorrect answers, and used for training.
As a result, ZPPO has the following characteristics.
- Preserves generalization: It does not directly inject the teacher's answers or imitate logits, thereby preventing Policy Drift
- Solves hard problems: It does not discard questions with very low accuracy but repeatedly exposes the model to them, raising Rollout Accuracy
- Efficient management: Once a question's accuracy reaches 50%, it is removed from the buffer (Graduate), optimizing training efficiency
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