Summary of Enhancing Multi-step Reasoning Abilities Of Language Models Through Direct Q-function Optimization, by Kaixuan Ji and Guanlin Liu and Ning Dai and Qingping Yang and Renjie Zheng and Zheng Wu and Chen Dun and Quanquan Gu and Lin Yan
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
by Kaixuan Ji, Guanlin Liu, Ning Dai, Qingping Yang, Renjie Zheng, Zheng Wu, Chen Dun, Quanquan Gu, Lin Yan
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 RL models are crucial in aligning large language models with human preferences and improving their ability to perform complex tasks. However, current approaches require significant computational resources or struggle with multi-step reasoning tasks. To overcome these limitations, we introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) using the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model. Experimental results on two math problem-solving datasets demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement Learning helps large language models do tasks humans prefer and can solve complex problems like math questions. But current ways need lots of computing power or struggle with thinking ahead. We created Direct Q-function Optimization (DQO) to fix this. It’s a new way to make big language models work better, using ideas from Markov Decision Processes and soft actor-critic learning. Our tests show DQO works better than before on math problems, making it a good way to use reinforcement Learning for big language models. |
Keywords
* Artificial intelligence * Language model * Optimization * Reinforcement learning
