A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Chia-Hsuan Lee, Sihui Dai, Mingyang Zhou, et al.
DASH assigns segment-level credit based on drift from correctness to reduce overthinking in reasoning language models, improving accuracy on competition math benchmarks.
S. Motwani, Alesia Ivanova, Ziyang Cai, et al.
Introduces a scalable method using curriculum RL on synthetically composed short-horizon data to boost long-horizon reasoning, achieving up to 2.06x accuracy gains on competition-level benchmarks.
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rStar-Coder constructs a large-scale verified dataset of 418K competition-level code problems with 580K long-reasoning solutions to improve LLM code reasoning.
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OpenMath Nemotron introduces a series of mathematical reasoning models trained on 540K problems and 3.2M solutions, achieving top performance in the AIMO-2 competition.
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MultiagentBench is a benchmark for evaluating LLM agents in multi-agent collaboration and competition scenarios.