Agentic In-Context Learning: What Algorithms Can Transformers Learn in Closed-Loop Interaction?
In brief
- Transformers can learn recursive algorithms via closed-loop interactions.
- Aligning process rewards with optimal preferences improves decision policies.
- Learned behaviors persist even after fine-tuning on varied text data.
This study introduces agentic in-context learning (Agentic ICL), where transformers interact with their environment to dynamically adjust actions. Researchers demonstrate that models can learn recursive algorithms through process or outcome rewards, without external state or action targets.
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