Summary of Latent Reward: Llm-empowered Credit Assignment in Episodic Reinforcement Learning, by Yun Qu et al.
Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
by Yun Qu, Yuhang Jiang, Boyuan Wang, Yixiu Mao, Cheems Wang, Chang Liu, Xiangyang Ji
First submitted to arxiv on: 15 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 A novel framework for reinforcement learning called LaRe is introduced, which leverages large language models to improve credit assignment. The framework addresses the challenges of delayed and sparse feedback in real-world applications by using a latent reward that evaluates performance from multiple perspectives. This approach enables more interpretable goal attainment and facilitates reward redistribution. The paper demonstrates that semantic code generated from LLM can bridge linguistic knowledge and symbolic latent rewards, and that latent reward self-verification increases the stability and reliability of LLM inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LaRe is a new way to help machines learn by making better decisions. Sometimes, machines get feedback only after they’ve made many decisions, which makes it hard for them to learn. LaRe helps solve this problem by using a special kind of reward that looks at how well the machine did from different angles. This makes it easier to understand what the machine is trying to achieve and to give it rewards when it does something right. The paper shows that LaRe works better than other methods for certain tasks. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning
