ClawLoop proposes self-improving training loop for harness-based AI agents

Key points

• Framework needs no external teacher model, using a loop of task synthesis, training, and diagnosis feedback.
• Qwen3.6-27B used as loop model; reported gains on SkillsBench, Claw-Eval-Live, and ClawGym-Bench.
• Refined tasks reportedly transfer to smaller Qwen3.5-4B and 9B models, improving results across all benchmarks.
• Preprint submitted October 9, 2026 on arXiv; the paper has not been peer reviewed.

Why it matters for marketers: Content and AI teams training agents may get a path to improve agents without relying on a stronger external model to generate training data.