Hugging Face Trending Papers
RetroChessBench: A Benchmark for Reconstructing and Proving Chess Histories
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arXiv AI
Oct 31
RetroChessBench: A Benchmark for Reconstructing and Proving Chess Histories
RetroChessBench is a 12‑task benchmark designed to evaluate language models on retrograde chess reasoning, requiring the reconstruction of legal game histories and the distinction between possible and forced events. The benchmark includes exact‑length constructions, unique endings, exhaustive answer sets, references, rubrics, and a terminal‑reward environment. In tests, GPT‑6 Astra High failed to produce correct final answers in 22 of 24 attempts without tools and 18 of 24 with code execution, highlighting significant reasoning failures even in rule‑governed tasks.
arXiv AI
Aug 7
Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models
arXiv:2605. 12519v2 Announce Type: replace-cross Abstract: Training language models to produce both correct answers and sound reasoning remains an open challenge.
arXiv Computation and Language
Oct 22
Disentangling generalization and memorization in large language models using chess
arXiv:2601.16823v3 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit remarkable capabilities, yet it remains unclear to what extent these reflect sophisticated recall or genuine r...
arXiv AI
Jul 20
Understanding Reasoning from Pretraining to Post-Training
arXiv:2607. 16097v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is largely studied in isolation from the pretraining that precedes it.
arXiv AI
Aug 18
Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability
arXiv:2604. 06628v2 Announce Type: replace Abstract: A prevailing narrative in LLM post-training holds that supervised finetuning (SFT) memorizes while reinforcement learning (RL) generalizes.
arXiv Machine Learning
Jul 27
LeAct: Learning to Reason from Expert Actions
arXiv:2607. 21856v1 Announce Type: new Abstract: Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs.