5 papers · 1 filter
When Do Intrinsic Rewards Work for Code Reasoning? A Comprehensive Study
Xiaolong Jin, Xuandong Zhao, Wenbo Guo +2
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in large language model reasoning, but relies on ground-truth supervision that is costly or in…
Skill-Guided Continuation Distillation for GUI Agents
Zhimin Fan, Hongwei Yu, Yeqing Shen +9
Improving GUI agents typically relies on behavior cloning on expert trajectories. However, as the current policy deviates from the expert policy, it inevitably encounters policy-in…
WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents
Lu Yan, Xuan Chen, Xiangyu Zhang
LLM agents are governed by long-lived prompt policies, where individually reasonable stand- ing rules can jointly govern the same pre- generation state. Existing instruction-follow…
Deep Researcher Agent: An Autonomous Framework for 24/7 Deep Learning Experimentation with Zero-Cost Monitoring
Xiangyue Zhang
We present \textbf{Deep Researcher Agent}, an open-source framework that enables large language model (LLM) agents to autonomously conduct deep learning experiments around the cloc…
StepFun-Prover Preview: Let's Think and Verify Step by Step
Shijie Shang, Ruosi Wan, Yue Peng +4
We present StepFun-Prover Preview, a large language model designed for formal theorem proving through tool-integrated reasoning. Using a reinforcement learning pipeline that incorp…