2 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
Spectral-Driven Adaptive-Rank Optimizer for Large Language Model Reinforcement Learning with Verifiable Rewards
Zhipeng Chen, Wayne Xin Zhao, Ji-Rong Wen
Reinforcement learning with verifiable rewards (RLVR) is reshaping how large language models (LLMs) acquire reasoning capabilities, yet the optimizers used remain largely inherited…
Evo-Bench: Can Language Models Improve Agent Harness?
Lisheng Huang, Chen Yang, Hao Zhou +6
Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolu…
ClawRec: A Claw-Native Recommender System
Chenghao Wu, Kesha Ou, Xiaolei Wang +8
Recommender systems have become integral to navigating the modern digital ecosystem. Yet most deployed systems remain confined within single-platform boundaries, observing localize…
SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training
Huatong Song, Lisheng Huang, Shuang Sun +11
In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master system…
SWE-World: Building Software Engineering Agents in Docker-Free Environments
Shuang Sun, Huatong Song, Lisheng Huang +11
Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedb…
Controlled LLM Training on Spectral Sphere
Tian Xie, Haoming Luo, Haoyu Tang +9
Scaling large models requires optimization strategies that ensure rapid convergence grounded in stability. Maximal Update Parametrization (P) provides a theoretical s…