VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit
  Assignment
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.
66fe2d6da77ec468fff0bdf7
Amirhossein Kazemnejad
claimed_verified
2024-10-04T07:31:53.800Z
63458f12d54fb141dedac508
/avatars/3946fb9c23d1cd24037770cc0a3489bf.svg
Amirhossein Kazemnejad
user
kazemnejad
66fe2d6da77ec468fff0bdf8
Milad Aghajohari
66fe2d6da77ec468fff0bdf9
Eva Portelance
66fe2d6da77ec468fff0bdfa
Alessandro Sordoni
66fe2d6da77ec468fff0bdfb
Siva Reddy
66fe2d6da77ec468fff0bdfc
Aaron Courville
66fe2d6da77ec468fff0bdfd
Nicolas Le Roux
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit
  Assignment
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.
VinePPO, a Monte Carlo-based method, improves reinforcement learning fine-tuning of large language models by accurately assigning credit to reasoning steps, outperforming PPO and RL-free baselines.
large language models reinforcement learning Proximal Policy Optimization value networks credit assignment Monte Carlo-based estimates MATH dataset GSM8K dataset