Summary of Self-evolved Reward Learning For Llms, by Chenghua Huang et al.
Self-Evolved Reward Learning for LLMs
by Chenghua Huang, Zhizhen Fan, Lu Wang, Fangkai Yang, Pu Zhao, Zeqi Lin, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 Self-Evolved Reward Learning (SER) approach in this paper tackles the challenge of training reliable reward models (RMs) for Reinforcement Learning from Human Feedback (RLHF). SER uses the RM to generate additional training data, allowing it to iteratively improve its performance. The authors conduct experiments on multiple datasets using various models and compare SER to baselines. The results demonstrate that even with limited human-annotated data, learning from self-feedback can robustly enhance RM performance, ultimately boosting the capabilities of large language models (LLMs). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better language models by finding a way for them to learn and improve on their own, without needing as much help from humans. The authors created a new method called Self-Evolved Reward Learning that lets the model generate its own training data. This makes it possible to train reward models even when there’s limited human input available. The results show that this approach can work well with different datasets and language models. |
Keywords
» Artificial intelligence » Boosting » Reinforcement learning from human feedback » Rlhf
