Summary of Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing, By Fangkai Jiao et al.
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing
by Fangkai Jiao, Chengwei Qin, Zhengyuan Liu, Nancy F. Chen, Shafiq Joty
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This research paper presents a novel framework for improving the reliability and faithfulness of Large Language Models (LLMs) in generating step-by-step rationales. The proposed Direct Preference Optimization (DPO) method leverages collected trajectories ranked according to synthesized process rewards, allowing LLMs to learn planning-based reasoning without high latency or human annotation costs. The authors demonstrate the effectiveness of their framework on challenging logical reasoning benchmarks, showcasing that a 7B model can outperform strong counterparts like GPT-3.5-Turbo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making computers better at explaining how they came up with answers. Right now, these computers are good at answering questions but not so great at telling us why. To fix this, the researchers created a new way for these computers to learn how to think step-by-step and come up with good explanations. They tested their method on some tricky problems and found that it worked really well! This is important because it could help us build more powerful computers that can do even more complex tasks. |
Keywords
» Artificial intelligence » Gpt » Optimization
