Zero-Distillation 4B Model Hits 61.5% on SWE-bench via Pure RL
rajammanabrolu · x · 2026-09-11
- FrogNano: a Qwen3.5-4B model post-trained purely with RL on synthetic TaskPilot tasks — zero distillation, just 5 iterations × 300 tasks — scores 61.5% on SWE-bench Verified, showing coding agents don't need to be huge.
- Multiple researchers echo a widely highlighted takeaway from DeepSeek's new tech report: RL environment design/synthesis is the core of training and likely scalable.
- A growing consensus that environment engineering matters more than model size.
More from coding & agent
- One prompt, full Blender animation: dev says he never touched Blender once — iamfakhrealam · 2026-10-26
- Claude Code finds root backdoor in Honda Civic head unit, then debloats it — IndraVahan · 2026-10-26
- Claude Code + Monid MCP builds a $12K/year B2B prospecting engine on live data — aryanXmahajan · 2026-10-26
- Karpathy shares CLAUDE.md rulebook to stop AI coding agents from piling up tech debt — mdancho84 · 2026-10-26
- Rethinking dependencies in the agent era: drop libs that save 20 lines of code — gethackteam · 2026-10-26
- RustGIS hits 2,000 GitHub stars in two weeks with agent-first architecture — giswqs · 2026-10-26