Agentic In-Context Learning: What Algorithms Can Transformers Learn in Closed-Loop Interaction?
The learning algorithm view of in-context learning (ICL) studies how fixed-parameter Transformers learn from supplied examples.
ProofPaper ↗
Key points
- Language model agents extend this setting through interaction: their actions determine what evidence enters context next.
- We call this regime Agentic In-Context Learning (Agentic ICL).
- We represent an interactive learner by its state, update, and decision rule, covering active learning, bandits, and online reinforcement learning (RL).
- Improving the alignment of process supervision with outcome-optimal action preferences shifts learned decisions accordingly.
Sources (1)
- [1]Agentic In-Context Learning: What Algorithms Can Transformers Learn in Closed-Loop Interaction?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 07:28 AM
The learning algorithm view of in-context learning (ICL) studies how fixed-parameter Transformers learn from supplied examples.
Language model agents extend this setting through interaction: their actions determine what evidence enters context next.
Extractive summary: sentences quoted from the sources.
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