{ "schema_version": 1, "builder_plan": { "scenario": "You already need task-specific training and want to reduce its memory or compute cost.", "change": "Change only the training technique on a small controlled run after checking prerequisites.", "baseline": "Your current training recipe with the same model, data split, and evaluation.", "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": "Held-out quality, peak memory, training time, and full compute cost to the target quality.", "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": "2609.39420", "title": "QuantCode Model: Specializing Language Models for Executable Algorithmic Trading Code", "summary": "The method replaces prompting a general-purpose code model with a two-stage specialization: continued pretraining on framework-specific code followed by SFT on agent-validated request-to-code pairs. It costs pretraining-scale compute plus a validation pipeline that executes generated strategies to filter training data, and the demonstrated results use 35B and 397B MoE models beyond a single 24 GB GPU. It can fail through degraded instruction following (continued pretraining alone lowered final agentic success from 47.5% to 32.5%) and through loss of parser-conformant tool calling, which recovery SFT only partially restores.", "rationale": "The two-stage recipe is validated but demonstrated at model scales the reader cannot train, so the actionable transfer is the SFT-on-executed-outputs pattern and the capability-retention evaluation, which the reader should watch rather than adopt directly.", "technique": "Domain continued pretraining plus validated SFT for trading code", "family": "treino_eficiente", "recommendation": "observar", "published": "2026-09-30", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.39420", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces prompting a general-purpose code model with a two-stage specialization: continued pretraining on framework-specific code followed by SFT on agent-validated request-to-code pairs. It costs pretraining-scale compute plus a validation pipeline that executes generated strategies to filter training data, and the demonstrated results use 35B and 397B MoE models beyond a single 24 GB GPU. It can fail through degraded instruction following (continued pretraining alone lowered final agentic success from 47.5% to 32.5%) and through loss of parser-conformant tool calling, which recovery SFT only partially restores.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.39420", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Continued pretraining raises Judge Pass from 41.5% to 47.5% (397B) and 27.8% to 33.0% (35B-A3B); SFT after pretraining reaches 58.2% Judge Pass, 83.5% successful backtests, and raises agentic first-turn success from 22.3% to 58.3% and final success from 47.5% to 79.5%.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.39420", "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_fetched", "core_kind": "", "equations_fetched_at": "", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }