Learning from the Right Rollouts: Data Attribution for PPO-based LLM Post-Training
Traditional RL algorithms like Proximal Policy Optimization (PPO) typically train on the entire rollout buffer, operating under the assumption that all generated episodes provide a beneficial optimization signal. However, these episodes frequently contain noisy or unfaithful reasoning, which can degrade model performance and slow down training. In this paper, we propose \textbf{Influence-Guided PPO (I-PPO)}, a novel framework that integrates data attribution into the RL post-training loop. By calculating an influence score for each episode using a gradient-based approximation, I-PPO identifies and eliminates episodes that are anti-aligned with a validation gradient. Our experiments demons...
| Authors | Dong Shu, Denghui Zhang, Jessica Hullman |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 24 August 2026 |
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