{ "schema_version": 1, "builder_plan": { "scenario": "A narrow, repeated product task may not need your largest model.", "change": "Check whether a released student model fits the task before generating training data.", "baseline": "Your current model and an off-the-shelf small model on the same held-out tasks.", "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": "Student quality, serving cost, teacher calls, training cost, and failures on unusual inputs.", "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": "2610.10447", "title": "A Good Self-Teacher Meets the Student Where They Are: Joint On-Policy Learning and Teaching", "summary": "JOLT replaces the separate stronger teacher model in on-policy distillation with a single jointly trained policy acting as a privileged, KL-regularized teacher of its own unprivileged student. It costs additional training complexity and compute over standard outcome-reward RL, since two roles of the same policy must be trained and token-level KL supervision computed on the student's own generations. It can fail if the privileged teacher exploits shortcuts unavailable to the student, in which case denser supervision can degrade student performance, and its benefits at small model scales under a single 24 GB GPU are unverified.", "rationale": "The method targets exactly the infrastructure-constrained setting by removing the separate teacher, but its RL training loop requires non-trivial implementation effort and workload-specific validation before production adoption.", "technique": "Joint on-policy self-distillation with KL-regularized privileged teacher", "family": "destilacao", "recommendation": "testar", "published": "2026-10-07", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.10447", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "JOLT replaces the separate stronger teacher model in on-policy distillation with a single jointly trained policy acting as a privileged, KL-regularized teacher of its own unprivileged student. It costs additional training complexity and compute over standard outcome-reward RL, since two roles of the same policy must be trained and token-level KL supervision computed on the student's own generations. It can fail if the privileged teacher exploits shortcuts unavailable to the student, in which case denser supervision can degrade student performance, and its benefits at small model scales under a single 24 GB GPU are unverified.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.10447", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "The paper states that JOLT improves training efficiency and performance across mathematical reasoning, coding, tool use, and terminal use, with further gains from student rewards, but the abstract provides no quantified figure.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.10447", "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": "" }