Academic

Learning from the Right Rollouts: Data Attribution for PPO-based LLM Post-Training

arXiv:2604.01597v1 Announce Type: new Abstract: 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 demonstrate that I-PPO consistently outperforms SFT and PPO baselines. We show that our filtering process acts as an intrinsic early stopping mechanism, accelerating training efficiency while effectively reducing unfaithful CoT reasoning.

D
Dong Shu, Denghui Zhang, Jessica Hullman · · 1 min read · 11 views
#cs.LG

arXiv:2604.01597v1 Announce Type: new Abstract: 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 demonstrate that I-PPO consistently outperforms SFT and PPO baselines. We show that our filtering process acts as an intrinsic early stopping mechanism, accelerating training efficiency while effectively reducing unfaithful CoT reasoning.

Executive Summary

This paper proposes a novel framework, Influence-Guided PPO (I-PPO), which integrates data attribution into the Proximal Policy Optimization (PPO) algorithm to improve the post-training efficiency of Large Language Models (LLMs). By calculating an influence score for each episode, I-PPO identifies and eliminates episodes that are anti-aligned with a validation gradient, effectively reducing unfaithful CoT reasoning. The authors demonstrate that I-PPO consistently outperforms SFT and PPO baselines, showcasing its potential as an intrinsic early stopping mechanism. The research contributes to the ongoing quest for more efficient and effective LLM training, which is crucial for widespread adoption in various applications. However, the method's applicability and generalizability to other RL algorithms and tasks remain to be explored.

Key Points

  • ▸ I-PPO integrates data attribution into the PPO algorithm to improve post-training efficiency
  • ▸ I-PPO eliminates episodes with anti-aligned validation gradients to reduce unfaithful CoT reasoning
  • ▸ I-PPO outperforms SFT and PPO baselines in experiments and acts as an intrinsic early stopping mechanism

Merits

Strength in Identifying Unfaithful Episodes

I-PPO's ability to identify and eliminate episodes with anti-aligned validation gradients is a significant merit, as it helps reduce the impact of noisy or unfaithful reasoning on model performance.

Improved Training Efficiency

I-PPO's intrinsic early stopping mechanism accelerates training efficiency, making it more practical for large-scale LLM training.

Demerits

Applicability to Other RL Algorithms

The authors' focus on PPO and the specific implementation of I-PPO raises questions about its applicability and generalizability to other RL algorithms and tasks.

Scalability and Computational Resources

The computational resources required to calculate influence scores for each episode might be significant, which could be a limitation for large-scale implementations.

Expert Commentary

The contribution of this paper lies in its novel approach to integrating data attribution into the PPO algorithm. I-PPO's ability to identify and eliminate episodes with anti-aligned validation gradients is a significant merit, as it helps reduce the impact of noisy or unfaithful reasoning on model performance. However, the method's applicability and generalizability to other RL algorithms and tasks remain to be explored. Additionally, the computational resources required to calculate influence scores for each episode might be significant, which could be a limitation for large-scale implementations. Nevertheless, I-PPO's improved training efficiency and ability to reduce unfaithful CoT reasoning make it a promising technique for large-scale LLM training in various applications.

Recommendations

  • ✓ Future research should explore I-PPO's applicability to other RL algorithms and tasks to determine its generalizability.
  • ✓ The authors should investigate methods to reduce the computational resources required for calculating influence scores, making I-PPO more scalable for large-scale implementations.

Sources

Original: arXiv - cs.LG

Related Articles