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NeurIPS 2026MIT / TUMHEC ParisTechnical University of MunichLLM evaluation & benchmarks

Disentangling generalization and memorization in large language models using chess

Chess reveals LLMs' reasoning collapses without memorized priors, with newer models showing diminishing gains on novel positions.

Leonard S. Pleiss, Maximilian Schiffer, Robert K von Weizsäcker

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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lenient 4/5
medium 7/10
strict 1/5
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NeurIPS 2026TU MunichU TwenteHEC ParisDeep RL

Breaking the Grid: Distance-Guided Reinforcement Learning in Large Discrete Action Spaces

DGRL enables efficient reinforcement learning in discrete action spaces up to 10^20 via distance-guided exploration and regression-based updates, improving performance by up to 66%.

Heiko Hoppe, Fabian Akkerman, Wouter van Heeswijk, Maximilian Schiffer

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5