Summary of Improving Multi-step Reasoning Abilities Of Large Language Models with Direct Advantage Policy Optimization, by Jiacai Liu and Chaojie Wang and Chris Yuhao Liu and Liang Zeng and Rui Yan and Yiwen Sun and Yang Liu and Yahui Zhou
Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization
by Jiacai Liu, Chaojie Wang, Chris Yuhao Liu, Liang Zeng, Rui Yan, Yiwen Sun, Yang Liu, Yahui Zhou
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper explores the role of reinforcement learning (RL) in enhancing the reasoning abilities of large language models (LLMs). Despite the success of RL in various scenarios, there are still challenges in improving the reasoning capabilities of LLMs. The authors identify two main issues: sparse rewards and instability in optimization processes. To address these challenges, they propose a novel algorithm called Direct Advantage Policy Optimization (DAPO), which uses a critic function to predict reasoning accuracy and generate dense signals for refining generation strategies. DAPO trains its actor and critic components independently, avoiding co-training instability observed in standard Actor-Critic algorithms like PPO. The authors evaluate DAPO’s performance on mathematical and code query datasets and multiple benchmarks, showing that it can effectively enhance the mathematical and code capabilities of both SFT models and RL models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new way to help big language models get better at solving math problems and understanding code. Right now, these models are good at doing certain tasks, but they struggle when faced with complex math problems or unfamiliar coding languages. The authors came up with a new method called DAPO (Direct Advantage Policy Optimization) that helps the models learn from their mistakes and improve over time. They tested this method on several datasets and found that it made a big difference in how well the models performed. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
