Improving Software Agents With Self-Distilled Knowledge
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Key Takeaways
- SkillForge addresses the cold-start problem in software agents by synthesizing repository-specific issues and capturing effective resolution trajectories.
- The framework organizes distilled knowledge into global diagnostic skills and local intervention skills for targeted agent reasoning.
- On SWE-bench Verified, SkillForge achieves 72.2% Pass@1 with DeepSeek-V3.2 and 60.6% Pass@1 with GPT-5-mini.
- SkillForge outperforms the Mini-SWE-Agent baseline across both SWE-bench Verified and SWE-bench Pro datasets.
Summary & Methodology Analysis
SkillForge functions by creating a specialized knowledge layer for software agents, helping them move beyond generic capabilities. The process begins by synthesizing project-specific issues: the system instructs an LLM to rewrite core repository functionalities, which triggers test failures. By solving these synthetic issues, the agent generates action trajectories that reveal successful patterns for navigating that specific codebase. These trajectories are then distilled, a process of condensing complex model knowledge into a more compact format, to create a dual-level skill repository containing global diagnostic skills and local intervention skills.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary problem SkillForge addresses?
It solves the cold-start problem where software agents lack the repository-specific knowledge needed to fix issues in unfamiliar codebases.
Q2. How does SkillForge learn new skills?
It synthesizes issues by forcing test failures and then distills successful action trajectories into a formal skill repository.
Q3. Does SkillForge work with any model?
It works with models like DeepSeek-V3.2 and GPT-5-mini, though the distilled knowledge is model-specific and not universally transferable.
Q4. What is the specific improvement over the baseline?
On SWE-bench Verified, it improves over Mini-SWE-Agent by 5.8 percentage points with DeepSeek-V3.2 and 5.6 percentage points with GPT-5-mini.
Q5. How does the system use the distilled skills?
Skills are retrieved and injected during reasoning using a macro-level initialization and a micro-level just-in-time injection mechanism.
Q6. What is the performance on the SWE-bench Pro dataset?
SkillForge improves over Mini-SWE-Agent by 5.8 percentage points with DeepSeek-V3.2 and 4.1 percentage points with GPT-5-mini.
Q7. Are there limitations regarding test coverage?
Yes, repositories with limited test coverage provide fewer learning signals, which can reduce the overall effectiveness of the acquired knowledge.
Q8. Can I use skills learned by one model on a different model?
No, the distilled project-specific knowledge is model-dependent and not universally transferable across different LLMs.
Q9. What specific agent framework was used for implementation?
The authors implemented SkillForge using the Mini-SWE-Agent framework.