Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study
arXiv:2610.28977v1 Announce Type: new Abstract: In-context reinforcement learning (ICRL) exploits the impressive in-context learning ability of large language models to zero-shot generalize to new, unseen tasks at inference time without any parameter updates. Among many approaches, the Decision-Pre…
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- 2026-10-22 04:00 · arXiv stat.ML
Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study