{ "schema_version": 1, "builder_plan": { "scenario": "Your coding assistant makes changes that still need substantial repair before merging.", "change": "Try the technique on held-out issues from a repository with reliable tests.", "baseline": "The same coding agent, repository snapshot, tools, and token budget.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Correct patches, regression failures, human repair time, and total cost per accepted change.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.16096", "title": "Coaching Qwen3 Coder 30B to Think Like a CodeClash Arena Agent", "summary": "The method replaces vanilla offline SFT on raw teacher traces with SFT over teacher trajectories rewritten into explicit observation-thought-action chains, plus sample reweighting that favors post-edit verification behavior. The cost is access to strong-agent teacher trajectories from the target environment, trajectory rewriting, and fine-tuning capacity for a 30B model, which exceeds a single 24 GB GPU without aggressive quantization and is easier via rented compute. It can fail when teacher behavior does not transfer to the reader's task distribution, when rewritten chains teach superficial reasoning formats, and because the gains are demonstrated only in one arena-style benchmark, not general coding workloads.", "rationale": "The idea is sound but adopting it requires 30B-scale fine-tuning and environment-specific teacher data that the reader's constrained infrastructure and budget do not readily support, so it should be monitored rather than adopted or tested now.", "technique": "ReAct SFT with trajectory-quality weighting", "family": "agentes_de_codigo", "recommendation": "observar", "published": "2026-09-14", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.16096", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces vanilla offline SFT on raw teacher traces with SFT over teacher trajectories rewritten into explicit observation-thought-action chains, plus sample reweighting that favors post-edit verification behavior. The cost is access to strong-agent teacher trajectories from the target environment, trajectory rewriting, and fine-tuning capacity for a 30B model, which exceeds a single 24 GB GPU without aggressive quantization and is easier via rented compute. It can fail when teacher behavior does not transfer to the reader's task distribution, when rewritten chains teach superficial reasoning formats, and because the gains are demonstrated only in one arena-style benchmark, not general coding workloads.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.16096", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "The fine-tuned Qwen3-Coder-30B outperforms the original Qwen3 Coder Plus in CodeClash tournament evaluation; no multiplicative factor is reported.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.16096", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compatibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "security", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "data_and_training", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "reproducibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" } ], "risks": [], "minimum_test": [], "open_questions": [], "independent_tests": [], "lab_reproductions": [], "report_available": false, "validation_tier": "unknown", "evidence_tier": "unknown", "infrastructure_basis": "unknown", "equations": [], "equations_status": "not_formula", "core_kind": "algorithm", "equations_fetched_at": "2026-10-10", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }