4 papers · 2 filters
Quality, Not Just Outcome: A Structural Scoring Framework for Selecting Coding-Agent Trajectories for Supervised Fine-Tuning
Sida Li, Yuanxin Liu, Shicheng Li +4
Open coding agents are commonly trained through supervised fine-tuning on interaction trajectories whose final patches pass the benchmark test suite. However, this outcome-level fi…
LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents
Yiming Du, Yuxin Jiang, Tao Yuan +9
Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execution feedback. However, the n…
What Makes Interaction Trajectories Effective for Training Terminal Agents?
Sidi Yang, Chaofan Tao, Jierun Chen +11
Stronger code agents are commonly assumed to be superior teachers for post-training, yet this assumption remains poorly disentangled from task difficulty, harness design, and stude…
QuantClaw: Precision Where It Matters for OpenClaw
Manyi Zhang, Ji-Fu Li, Zhongao Sun +5
Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long-context inputs and multi-turn reasoning. This results in prohibitively high comput…