6 papers
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
Guhong Chen, Yingcheng Shi, Yongbin Li +6
Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static. This limitation sharpens in agentic RL, where shifting bottlenecks and s…
ESPO: Early-Stopping Proximal Policy Optimization
Zihang Li, Rui Zhou, Yingcheng Shi +8
When a large language model under reinforcement learning commits a wrong reasoning step early in a trajectory, standard algorithms force it to keep generating until the maximum hor…
CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoning
Zhiyuan Lu, Chenliang Li, Yingcheng Shi +3
While large language models now handle million-token contexts, their capacity for reasoning across entire document repositories remains largely untested. Existing benchmarks are in…
QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management
Weizhou Shen, Ziyi Yang, Chenliang Li +11
We introduce QwenLong-L1.5, a model that achieves superior long-context reasoning capabilities through systematic post-training innovations. The key technical breakthroughs of Qwen…
QwenLong-CPRS: Towards -LLMs with Dynamic Context Optimization
Weizhou Shen, Chenliang Li, Fanqi Wan +12
This technical report presents QwenLong-CPRS, a context compression framework designed for explicit long-context optimization, addressing prohibitive computation overhead during th…
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Fanqi Wan, Weizhou Shen, Shengyi Liao +7
Recent large reasoning models (LRMs) have demonstrated strong reasoning capabilities through reinforcement learning (RL). These improvements have primarily been observed within the…