Summary of Vineppo: Unlocking Rl Potential For Llm Reasoning Through Refined Credit Assignment, by Amirhossein Kazemnejad et al.
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment
by Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, Nicolas Le Roux
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an approach called VinePPO, which is designed to improve the performance of large language models (LLMs) on complex reasoning tasks. The authors identify limitations with existing value networks used in Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm. They show that these value networks struggle to predict expected cumulative rewards accurately, leading to high-variance updates and suboptimal performance. To address this, VinePPO leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates, bypassing the need for large value networks. The approach is evaluated on MATH and GSM8K datasets, demonstrating consistent outperformance over PPO and other RL-free baselines with fewer gradient updates (up-to 9x) and less wall-clock time (up-to 3.0x). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VinePPO is a new way to help large language models do better on tricky tasks that require many steps. Right now, these models use something called value networks to figure out what’s working well, but this doesn’t always work very well. The authors of VinePPO showed that value networks don’t do very well when the task gets harder. So they came up with a new way to make it easier for the model to learn. This approach uses language environments in a special way to help the model get better without needing big value networks. It works really well and is faster than what’s currently being used. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
