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In-Context RL Models May Not Be Bayesian Posterior Samplers in Practice

By PulseAugur Editorial · [1 sources] ·

A new research paper investigates whether in-context reinforcement learning (ICRL), specifically the Decision-Pretrained Transformer (DPT) model, functions as a practical Bayesian posterior sampler. The study establishes two necessary conditions for Bayesian posterior samplers: sufficient-statistic invariance and the martingale property. Using statistical hypothesis tests in Gaussian, Bernoulli, and linear bandit settings, the researchers found that these conditions are consistently violated in practically trained ICRL models. This suggests that ICRL may not behave as Bayesian posterior samplers in real-world applications, though the developed testing framework offers a principled way to assess the Bayesian behavior of ICRL. AI

IMPACT Challenges the theoretical underpinnings of in-context learning, potentially guiding future research toward more robust and predictable model behaviors.

RANK_REASON Research paper published on arXiv detailing empirical study of model behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

In-Context RL Models May Not Be Bayesian Posterior Samplers in Practice

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Research paper published on arXiv detailing empirical study of model behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Weiqin Chen, Xinlong Du, Gesualdo Scutari, Harsha Honnappa ·

    Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study

    arXiv:2610.28977v1 Announce Type: cross 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 approac…

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