title: VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit
    Assignment
summary: 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.
paper_authors._id: 66fe2d6da77ec468fff0bdf7
paper_authors.name: Amirhossein Kazemnejad
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-04T07:31:53.800Z
paper_authors._id: 63458f12d54fb141dedac508
paper_authors.avatarUrl: /avatars/3946fb9c23d1cd24037770cc0a3489bf.svg
paper_authors.fullname: Amirhossein Kazemnejad
paper_authors.type: user
paper_authors.user: kazemnejad
paper_authors._id: 66fe2d6da77ec468fff0bdf8
paper_authors.name: Milad Aghajohari
paper_authors._id: 66fe2d6da77ec468fff0bdf9
paper_authors.name: Eva Portelance
paper_authors._id: 66fe2d6da77ec468fff0bdfa
paper_authors.name: Alessandro Sordoni
paper_authors._id: 66fe2d6da77ec468fff0bdfb
paper_authors.name: Siva Reddy
paper_authors._id: 66fe2d6da77ec468fff0bdfc
paper_authors.name: Aaron Courville
paper_authors._id: 66fe2d6da77ec468fff0bdfd
paper_authors.name: Nicolas Le Roux
paper_title: VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit
    Assignment
paper_summary: 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.
paper_ai_keywords: Large language models (LLMs) complex reasoning tasks Proximal Policy Optimization (PPO) reinforcement learning (RL) value networks credit assignment high-variance updates suboptimal performance VinePPO unbiased Monte Carlo-based estimates gradient updates wall-clock time MATH datasets GSM8K datasets RL-free baselines accurate credit assignment superior alternative