Summary of Skill Set Optimization: Reinforcing Language Model Behavior Via Transferable Skills, by Kolby Nottingham et al.
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
by Kolby Nottingham, Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Sameer Singh, Peter Clark, Roy Fox
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 Medium Difficulty Summary: Large language models (LLMs) are being used for decision-making in interactive environments, but improving their performance through environment reward signals is challenging. The proposed Skill Set Optimization (SSO) method constructs and refines sets of transferable skills to improve LLM actor performance. SSO extracts common subtrajectories with high rewards, generates subgoals and instructions, and provides these skills to the LLM in-context to reinforce high-reward behaviors. It then prunes skills that no longer result in high rewards. This method outperforms baselines by 40% in a custom NetHack task and outperforms previous state-of-the-art methods by 35% in ScienceWorld. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Researchers are using big language models to make decisions in games and other interactive environments. The problem is that it’s hard to make these models better using the rewards they get from the environment. A new method called Skill Set Optimization helps solve this problem by creating and refining sets of skills that can be used to improve the model’s performance. This method works by looking for patterns in the data that are associated with high rewards, and then providing those patterns to the model as goals to work towards. The results show that this method is better than other methods at improving the model’s performance. |
Keywords
* Artificial intelligence * Optimization
