arXiv Machine Learning
Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jul 1
Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
arXiv:2606. 31184v1 Announce Type: cross Abstract: Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency.
arXiv AI
Jun 19
Can In-Context Learning Support Intrinsic Curiosity?
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.
arXiv AI
Aug 5
In-Context Pure Exploration in Continuous Decision Spaces
arXiv:2602. 17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible.
arXiv AI
Oct 2
Finetuning with Sampling: SFT Learns Better Than You Think
The paper introduces a Markov chain Monte Carlo (MCMC) sampling algorithm that transforms off‑policy traces into more on‑policy data for supervised finetuning (SFT). By tailoring the data distribution rather than changing the learning objective, the method allows SFT to match or surpass traditional post‑training techniques such as reinforcement learning across tasks like scientific skill acquisition, mathematical reasoning, and open‑ended expertise. The resulting models generalize better, forget less, and exhibit strong distributional performance, demonstrating that sampling can serve as a model‑native operator for broader post‑training applications.
Hugging Face Trending Papers
Jun 17
Can In-Context Learning Support Intrinsic Curiosity?
Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the problem of automated data selection, or "intrinsic curiosity", remains a significant challenge.
arXiv AI
Jul 9
When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning
arXiv:2607. 06720v1 Announce Type: new Abstract: Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise solution attempts.