Summary of Cpl: Critical Plan Step Learning Boosts Llm Generalization in Reasoning Tasks, by Tianlong Wang et al.
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks
by Tianlong Wang, Junzhe Chen, Xueting Han, Jing Bai
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 proposes Critical Plan Step Learning (CPL), a novel approach for training large language models (LLMs) in reinforcement learning (RL). Existing RL methods focus on task-specific reasoning, whereas CPL aims to develop general reasoners capable of solving problems effectively across a broader range of tasks. The proposed method combines Monte Carlo Tree Search (MCTS) and Step-level Advantage Preference Optimization (Step-APO) to search for valuable and diverse strategies within the infinite action space of LLMs. Experimental results demonstrate significant improvements in performance on various benchmarks, including GSM8K, MATH, HumanEval, GPQA, ARC-C, MMLU-STEM, and BBH. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make big language models better at solving problems by themselves. Right now, these models are really good at doing specific tasks, but they’re not very good at coming up with new solutions or applying what they know to different situations. The researchers propose a new way of training these models called Critical Plan Step Learning (CPL). This method helps the models find better ways to solve problems by exploring many different options and choosing the best ones. The results show that this approach leads to big improvements in how well the models can solve problems on their own. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
