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cs.LG2026
Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents
Kaijie Chen, Chenyu Fang, Liang Yan +3
Test-time self-evolving agents improve by reusing past experience, yet sparse-reward trajectories contain failures, loops, and detours, while summaries often omit the state conditi…
cs.LG2026
ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research
Wanghan Xu, Shuo Li, Tianlin Ye +48
AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchma…
cs.LG2026
Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning
Zhiyin Yu, Bo Zhang, Qibin Hou +3
Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entropy-based rewards. However, the…