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Research & Blogs
10 min read
Lessons Learned from LegoFlow-SWE
What task quality, reward hacking, and reasoning depth taught us about building reliable coding-agent training data.
15 min read
LegoFlow: Easy and Interactive Code Data Engineering
An agent-native framework for curating coding tasks, collecting trajectories, training models, and evaluating the full loop.
8–12 min read
Lego-RL: Harness-Native Reinforcement Learning for Coding Agents
Without modifying OpenHands SDK, Claude Code, or OpenCode, Lego-RL plugs native harnesses into GSPO training. The same Qwen3.5-35B-A3B gains +6.4 / +5.8 / +9.4 on SWE-bench Verified.
7–9 min read
SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review
An agentic reviewer that explores the repository, traces root causes, and returns structured feedback to close the generate–review–revise loop.
8–10 min read
What Makes Interaction Trajectories Effective for Training Terminal Agents?
Why the strongest agent is not always the best teacher, and how environment-grounded supervision enables far more data-efficient terminal-agent training.
8–10 min read
SWE-Lego: Pushing the Limits of Supervised Fine-Tuning for Software Issue Resolving
A supervised fine-tuning recipe built from curated instances, validated trajectories, error masking, a difficulty curriculum, and a test-time verifier.