VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit
  Assignment
Authors: name: Amirhossein Kazemnejad, name: Milad Aghajohari, name: Eva Portelance, name: Alessandro Sordoni, name: Siva Reddy, name: Aaron Courville, name: Nicolas Le Roux

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 state-of-the-art
reinforcement learning (RL) algorithm used for LLM finetuning, employs value
networks to tackle credit assignment. However, value networks face challenges
in predicting the expected cumulative rewards accurately in complex reasoning
tasks, often leading to high-variance updates and suboptimal performance. In
this work, we systematically evaluate the efficacy of value networks and reveal
their significant shortcomings in reasoning-heavy LLM tasks, showing that they
barely outperform a random baseline when comparing alternative steps. To
address this, we propose VinePPO, a straightforward approach that leverages the
flexibility of language environments to compute unbiased Monte Carlo-based
estimates, bypassing the need for large value networks. Our method consistently
outperforms PPO and other RL-free baselines across MATH and GSM8K datasets with
fewer gradient updates (up to 9x), less wall-clock time (up to 3.0x). These
results emphasize the importance of accurate credit assignment in RL finetuning
of LLM and demonstrate VinePPO's potential as a superior alternative.