Summary of Online Preference-based Reinforcement Learning with Self-augmented Feedback From Large Language Model, by Songjun Tu et al.
Online Preference-based Reinforcement Learning with Self-augmented Feedback from Large Language Model
by Songjun Tu, Jingbo Sun, Qichao Zhang, Xiangyuan Lan, Dongbin Zhao
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed RL-SaLLM-F technique for preference-based reinforcement learning (PbRL) enables online learning without relying on privileged predefined rewards or human feedback. By leveraging the reflective and discriminative capabilities of large language models (LLMs), RL-SaLLM-F generates self-augmented trajectories and provides preference labels for reward learning. This approach mitigates query ambiguity in LLM-based preference discrimination, leading to improved quality and efficiency of feedback. The double-check mechanism further ensures reliability by reducing randomness in preference labels. Experimental results across multiple tasks in the MetaWorld benchmark demonstrate the effectiveness of RL-SaLLM-F in replacing impractical “scripted teacher” feedback. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for computers to learn from humans without needing special help or predefined rules. The method, called RL-SaLLM-F, uses large language models (LLMs) to create new scenarios and decide which ones are good or bad based on what they’ve learned before. This makes it possible for machines to learn from people online without needing any special instructions or feedback. The approach is shown to be effective in several different tasks, making it a useful tool for building more intelligent machines. |
Keywords
» Artificial intelligence » Online learning » Reinforcement learning
