Summary of On the Modeling Capabilities Of Large Language Models For Sequential Decision Making, by Martin Klissarov et al.
On the Modeling Capabilities of Large Language Models for Sequential Decision Making
by Martin Klissarov, Devon Hjelm, Alexander Toshev, Bogdan Mazoure
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Large Language Models (LLMs) have shown impressive performance in various reasoning and planning tasks. This paper investigates their capabilities for reinforcement learning (RL) across different interactive domains. The authors evaluate LLMs’ ability to produce decision-making policies, either directly or indirectly, by generating rewards models. Results show that LLMs excel at reward modeling without task-specific fine-tuning. Crafting rewards with AI feedback yields the most generally applicable approach and enhances performance. Fine-tuning LLMs with synthetic data can improve reward modeling capabilities while mitigating catastrophic forgetting, expanding their utility in sequential decision-making tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine machines that can make smart decisions like humans do. This paper looks at how well these “Large Language Models” (LLMs) can learn to make good choices in different situations. The researchers tested the LLMs’ ability to come up with plans and make decisions, either directly or by creating a special kind of feedback that helps them learn. They found that the LLMs are really good at coming up with ways to reward themselves for making good choices. This can help them learn and improve over time. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning » Synthetic data
