Summary of Reinforcing Language Agents Via Policy Optimization with Action Decomposition, by Muning Wen et al.
Reinforcing Language Agents via Policy Optimization with Action Decomposition
by Muning Wen, Ziyu Wan, Weinan Zhang, Jun Wang, Ying Wen
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a novel approach to optimizing language models as intelligent agents, addressing limitations in current methods such as GLAM and TWOSOME. The proposed method, Policy Optimization with Action Decomposition (POAD), decomposes optimization from the action level to the token level, providing finer supervision for each intra-action token and manageable complexity. POAD is implemented within the PPO algorithm and benefits from a more accurate credit assignment process and lower optimization complexity, leading to enhanced learning efficiency and generalization abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are intelligent agents that can make decisions based on sequential input, but they struggle with limited knowledge of environmental dynamics and huge action spaces. The authors propose a new way of optimizing language models by breaking down the problem into smaller steps at the token level, rather than the action level. This approach provides more accurate feedback to the model and makes it easier to optimize. |
Keywords
* Artificial intelligence * Generalization * Optimization * Token
