Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite
Quick summary
arXiv:2610.02826v1 Announce Type: new Abstract: Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that may be unavailable during deployment. We propose Recursive Self-Rewrite (RSR), a framework that uses one base model, Qwen-3.8-27B, to discover successful solutions under diverse harnesses and reconstruct them as training trajectories under a general harness. A planner extracts procedures into runbooks, a critic screens for verifier and solution leakage and guides recursive revision, and an executor
Key takeaways
- arXiv:2610.02826v1 Announce Type: new Abstract: Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that may be unavailable during deployment.
- We propose Recursive Self-Rewrite (RSR), a framework that uses one base model, Qwen-3.8-27B, to discover successful solutions under diverse harnesses and reconstruct them as training trajectories under a general harness.
- A planner extracts procedures into runbooks, a critic screens for verifier and solution leakage and guides recursive revision, and an executor
Why it matters
The significance goes beyond a temporary access problem: “Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite” exposes the operational cost of depending on one AI provider. Critical tasks need predefined fallback, queueing and human-continuation paths.
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