Summary of Rlsf: Reinforcement Learning Via Symbolic Feedback, by Piyush Jha et al.
RLSF: Reinforcement Learning via Symbolic Feedback
by Piyush Jha, Prithwish Jana, Pranavkrishna Suresh, Arnav Arora, Vijay Ganesh
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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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 proposes an innovative approach to fine-tuning Large Language Models (LLMs) using Reinforcement Learning with Human Feedback (RLHF). The authors highlight limitations in current RLHF methods, including unsound reward models, data collection challenges, and reliance on sparse scalar rewards. They demonstrate that these approaches often struggle when applied to tasks requiring complex domain-specific understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores ways to improve fine-tuning of Large Language Models (LLMs) using human feedback. Researchers face problems like bad reward systems, trouble collecting helpful data, and relying on simple numbers. These methods don’t work well for tasks that need special knowledge. The goal is to make RLHF better. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning » Rlhf
