VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment
Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, Nicolas Le Roux
2024-10-04
Summary
This paper introduces VinePPO, a new method designed to improve how large language models (LLMs) learn from complex reasoning tasks by refining how credit is assigned to different steps in the learning process.
What's the problem?
When training LLMs for tasks that require multiple steps of reasoning, it's important to properly assign credit to each step to help the model learn effectively. Current methods, like Proximal Policy Optimization (PPO), use value networks to predict rewards, but these networks often struggle with accuracy in complex tasks. This can lead to inconsistent learning and poor performance, as they sometimes provide unreliable feedback on which steps were helpful.
What's the solution?
To address this issue, the authors developed VinePPO, which simplifies the process of assigning credit by using unbiased estimates based on Monte Carlo sampling instead of relying on large value networks. This means that VinePPO can evaluate the effectiveness of each step more accurately and efficiently. The new method allows for faster training with fewer updates needed, making it easier for LLMs to learn from their experiences. In tests using datasets like MATH and GSM8K, VinePPO consistently outperformed traditional PPO methods and other approaches that don't use reinforcement learning.
Why it matters?
This research is important because it enhances the ability of LLMs to tackle complex reasoning tasks more effectively. By improving how credit is assigned during training, VinePPO can lead to better performance in applications that require advanced reasoning skills, such as problem-solving in mathematics or understanding intricate instructions, ultimately making AI systems more capable and reliable.
Abstract
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.