AIME26
aime26-109cd396·2 events·first seen 1mo agoAliases: AIME26
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Entropy-Cut Metropolis-Hastings: Sampling-Based Reasoning Without RL Training
This paper introduces Entropy-Cut Metropolis-Hastings (ECMH), an algorithm that samples from a 'power distribution' over base language model outputs to elicit strong reasoning without reinforcement learning posttraining. Rather than cutting reasoning traces at uniformly random positions, ECMH uses next-token entropy as a proxy to identify consequential decision points (e.g., choice of proof strategy), then resamples from those positions. The authors prove that mixing time scales with the number of decisions rather than tokens, and demonstrate consistent improvements over RL-trained models on MATH500, HumanEval, GPQA Diamond, and AIME26.
Data Points: Thinking Machines Interaction Model, ERNIE 5.1, Co-Mathematician, RL Conductor, and More
This edition of The Batch covers five notable AI developments: Thinking Machines' research preview of an 'interaction model' with a 200ms micro-turn multimodal architecture; Baidu's ERNIE 5.1, a compressed derivative of ERNIE 5.0 using only 6% of typical pre-training compute; Google DeepMind's Co-Mathematician collaborative workbench reaching 48% on FrontierMath Tier 4; a 7B RL Conductor model that orchestrates multi-agent workflows via reinforcement learning; and Google's Magic Pointer cursor system powered by Gemini. Secondary items include GitHub Copilot pricing restructuring ahead of usage-based billing.