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VinePPO: Refining Credit Assignment in RL Training of LLMs

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arxiv 2410.01679 v2 pith:ZEVSCJFX submitted 2024-10-02 cs.LG cs.CL

VinePPO: Refining Credit Assignment in RL Training of LLMs

Amirhossein Kazemnejad , Milad Aghajohari , Eva Portelance , Alessandro Sordoni , Siva Reddy , Aaron Courville , Nicolas Le Roux This is my paper
classification cs.LG cs.CL
keywords creditassignmenttrainingllmsnetworksstepsvalueaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receiving any reward. Properly assigning credit to these steps is essential for enhancing model performance. Proximal Policy Optimization (PPO), a common reinforcement learning (RL) algorithm used for LLM finetuning, employs value networks to tackle credit assignment. However, recent approaches achieve strong results without it, raising questions about the efficacy of value networks in practice. In this work, we systematically evaluate the efficacy of value networks and reveal their significant shortcomings in reasoning-heavy LLM tasks, showing that they often produce poor estimate of expected return and barely outperform a random baseline when comparing alternative steps. This motivates our key question: Can improved credit assignment enhance RL training for LLMs? To address this, we propose VinePPO, a straightforward approach that leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates. Our method consistently outperforms PPO and other baselines across MATH and GSM8K datasets in less wall-clock time (up to 3.0x). Crucially, it achieves higher test accuracy for a given training accuracy, capturing more generalization signal per sample. These results emphasize the importance of accurate credit assignment in RL training of LLM.

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Cited by 34 Pith papers

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