Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
📰 ArXiv cs.AI
Improve sequential decision-making with large language models using supervised fine-tuning for better in-context learning
Action Steps
- Fine-tune a pre-trained large language model using supervised learning on a sequential decision-making task
- Apply the fine-tuned model to a Markov Decision Process (MDP) or Partially Observable MDP (POMDP) to evaluate its performance
- Use the model to make decisions in an Ambiguous POMDP (APOMDP) setting, where uncertainty is present
- Compare the performance of the fine-tuned model with other approaches, such as reinforcement learning
- Test the model's ability to generalize to new, unseen tasks or environments
Who Needs to Know This
Researchers and engineers working on sequential decision-making tasks, such as those in robotics or game playing, can benefit from this approach to improve their models' performance
Key Insight
💡 Supervised fine-tuning can significantly improve the in-context learning capabilities of large language models for sequential decision-making tasks
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Boost sequential decision-making with supervised fine-tuning of large language models! #LLMs #SequentialDecisionMaking
Key Takeaways
Improve sequential decision-making with large language models using supervised fine-tuning for better in-context learning
Full Article
Title: Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
Abstract:
arXiv:2605.09009v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities, yet their potential for sequential decision-making remains underexplored. In this paper, we study the ICL capabilities of LLMs in sequential decision-making settings, including Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and Ambiguous POMDPs (APOMDPs). We fine-tune pretrained LLMs to perform few-shot decision-making directly from o
Abstract:
arXiv:2605.09009v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities, yet their potential for sequential decision-making remains underexplored. In this paper, we study the ICL capabilities of LLMs in sequential decision-making settings, including Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and Ambiguous POMDPs (APOMDPs). We fine-tune pretrained LLMs to perform few-shot decision-making directly from o
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