Source note
Scaling Coding Agents via Atomic Skills
Summary
This paper argues that coding agents generalize better when trained on a small set of reusable software engineering skills instead of end-to-end task benchmarks. It defines five atomic skills and trains one shared agent with joint reinforcement learning across all of them.
Problem
- Current coding agents are often trained on composite tasks such as bug fixing, and the paper claims this leads to task-specific overfitting and weak transfer to other software engineering tasks.
- Composite tasks are hard to scale with RL because the task space is broad and reward design for each new task is costly and inconsistent.
- The authors want a training target that is easier to evaluate, reusable across workflows, and more likely to transfer to unseen tasks.
Approach
- The paper defines five atomic skills: code localization, code editing, unit-test generation, issue reproduction, and code review.
- Each skill has a concrete input-output format and an execution-based reward: exact file-match for localization, all-tests-pass for editing, bug-detecting tests for unit-test generation, correct binary judgment for review, and pre-patch fail plus post-patch pass for issue reproduction.
- A single shared policy is initialized with light SFT on 1,500 verified trajectories, 300 per skill, using GLM-4.5-Air-Base as the starting model.
- The agent is then trained with joint RL over a unified task buffer, using GRPO to compare multiple sampled outputs for the same input and reduce reward-scale mismatch across skills.
- Training runs in sandboxed environments with only bash and str_replace tools, 10,000+ concurrent sandboxes, and 25,000+ pre-built Docker images.
Results
- The main claim is an average 18.7% performance gain across 10 tasks: 5 atomic skills and 5 composite tasks.
- On atomic skills (Avg@3), the SFT+RL model improves over SFT on all five skills: code localization 0.665 -> 0.712, code editing 0.458 -> 0.611, issue reproduction 0.542 -> 0.605, unit-test generation 0.359 -> 0.472, and code review 0.563 -> 0.622.
- On unseen composite tasks (Avg@3), SFT+RL improves over SFT on all five benchmarks: SWE-bench Verified 0.507 -> 0.585, SWE-bench Multilingual 0.300 -> 0.389, Terminal-Bench 2.0 0.151 -> 0.182, Code Refactoring 0.146 -> 0.171, and SEC-Bench 0.136 -> 0.169.
- Averaged over all reported tasks, joint RL reaches 0.452 Avg@3 versus 0.383 for the SFT model and 0.416 for GLM-4.5-Air.
- Against GLM-4.5-Air on atomic skills, the joint RL model is higher on localization 0.712 vs 0.666, editing 0.611 vs 0.556, issue reproduction 0.605 vs 0.555, unit-test generation 0.472 vs 0.423, and review 0.622 vs 0.536.
- On composite benchmarks versus GLM-4.5-Air, the joint RL model is higher on SWE-bench Verified 0.585 vs 0.559, SWE-bench Multilingual 0.389 vs 0.358, Code Refactoring 0.171 vs 0.159, and SEC-Bench 0.169 vs 0.163, but lower on Terminal-Bench 2.0 0.182 vs 0.187.