{
  "schema": "AutonomaFableRouterCurriculum.v2",
  "status": "data_contract_frozen_training_not_authorized_nonrouting",
  "nonRouting": true,
  "trainingAuthorized": false,
  "goalOrder": [
    "capability",
    "succinctness_and_termination",
    "expert_efficiency",
    "throughput"
  ],
  "immutableSources": {
    "host": {
      "repo": "ermiaazarkhalili/LFM2.5-2.6B-SFT-Fable5",
      "revision": "72d68fc"
    },
    "reasoning": {
      "repo": "HelioAI/Claude-Fable-5-5500x",
      "revision": "440267fbdb1b00a40216e7233dbce25530a0ed09"
    },
    "agentCoding": {
      "repo": "greghavens/fable-5-coding-and-debugging-traces",
      "revision": "c63e82adec30798edcbd6e1dcb0014d2b15de236"
    },
    "selectedBanks": {
      "path": "config/benching/fable-donor-selected-banks.v1.json",
      "sha256": "158689cca0679423925cfe3c2eefbd6a248045ed01d2f7dad4d74e05e3ad0d81"
    }
  },
  "dataPolicy": {
    "splitIdentity": {
      "reasoning": "sha256(trimmed prompt)",
      "agentCoding": "source_trajectory_sha256"
    },
    "splitBuckets": {
      "train": "0..899",
      "validation": "900..949",
      "test": "950..999"
    },
    "reasoningDeduplication": "retain the shortest admitted non-empty completion per normalized prompt",
    "historicalTrajectorySampling": "at most the first tool-producing prefix plus the final prefix",
    "attestedTrajectorySampling": "retain all verifier-attested assistant decisions",
    "maximumTokensBeforeTemplate": 1900,
    "finalTemplateMaximumTokens": 2048,
    "finalTrainerMustRetokenizeAndRejectOverflow": true,
    "frozenEvaluationIsolation": "never read sealed/frozen evaluation prompts or answers while building or training"
  },
  "lanes": {
    "host_preservation": {
      "routerEligibility": "preserve",
      "sftWeight": 1.0,
      "baseFableKlWeight": 1.0,
      "expertActivityTarget": "zero unless routed loss beats frozen host by the benefit margin"
    },
    "verified_expert": {
      "routerEligibility": "expert",
      "sftWeight": 1.0,
      "baseFableKlWeight": 0.25,
      "requires": "model_attested=true and non-empty verifier"
    },
    "interaction_pattern": {
      "routerEligibility": "conditional",
      "sftWeight": 0.1,
      "baseFableKlWeight": 0.5,
      "purpose": "tool topology and recovery only; not correctness supervision"
    },
    "loop_negative": {
      "routerEligibility": "inherit_positive",
      "preferenceWeight": 0.5,
      "purpose": "reject repeated completions and duplicate identical tool calls"
    }
  },
  "routingObjective": {
    "counterfactual": "compare frozen-host token NLL with independently explored routed token NLL; never rely only on the router's current choice",
    "offClass": "host-only is an explicit route target when no sampled expert helps",
    "exploration": {
      "expertScaleDuringDiscovery": "initialize at 0.005 and cap at 0.025 so incorrect trials cannot materially override the host",
      "oracleProbeScales": [
        0.025,
        0.05,
        0.1
      ],
      "probeIsolation": "counterfactual probe branches are detached from the served/main output; only the low-scale chosen branch can affect host logits during discovery",
      "candidateExpertsPerEligibleToken": 4,
      "sampling": "stratified without replacement across steps and layers, independent of router logits; half uniform coverage and half frozen-profile-prior sampling",
      "coverageBeforePruning": "every selected expert must receive counterfactual trials on validation-eligible token classes",
      "forcedExplorationProbability": {
        "initial": 0.25,
        "final": 0.05,
        "annealSteps": 300
      },
      "eligibleTokenEntropyBonus": {
        "initial": 0.01,
        "final": 0.0,
        "annealSteps": 300
      },
      "minimumEligibleRouteMass": {
        "initial": 0.15,
        "final": 0.0,
        "annealSteps": 300,
        "scope": "expert-eligible tokens only; never host-preservation tokens"
      },
      "positiveDiscoveryReplay": "retain positive-benefit token/expert pairs in a bounded balanced replay buffer"
    },
    "routerSupervision": {
      "target": "best sampled positive-benefit expert, with host-only off as a competing class",
      "loss": "benefit-weighted ranking/classification plus end-to-end token NLL",
      "negativeExperts": "sampled wrong experts teach relative ranking without requiring their high-impact activation",
      "allOffProtection": "ranking targets and the temporary eligible-token route floor provide gradients even when current router logits prefer off"
    },
    "benefitMarginNats": 0.02,
    "benefitMarginSchedule": "0.0 during discovery, anneal to 0.02 nats over steps 100..300",
    "routeActivation": "after discovery, reward only when routed NLL improves beyond the annealed margin",
    "routeCost": {
      "expertScaleL1": 0.015,
      "activeExpertPenalty": 0.005,
      "targetActiveExpertsPerEligibleToken": "1..2",
      "schedule": "zero for the first 100 discovery steps, then linearly anneal to full weight by step 300"
    },
    "conditionalLoadBalance": {
      "weight": 0.01,
      "applyOnlyTo": "expert-eligible tokens that pass the benefit gate",
      "neverApplyTo": [
        "host_preservation tokens",
        "tokens where the host-only path is equal or better"
      ]
    },
    "antiDeadExpert": {
      "measurement": "usage and marginal NLL improvement per layer, expert, lane, and donor bank",
      "minimumUse": "no global quota; require coverage only among positive-benefit eligible tokens",
      "pruneRule": "remove experts only after completed stratified exploration finds no validation-set marginal benefit; router non-use alone is not evidence",
      "collapseGate": "reject checkpoints that choose off for all eligible tokens while the oracle replay buffer contains positive-benefit routes"
    }
  },
  "behaviorObjective": {
    "stopBoundaryLoss": 0.25,
    "loopUnlikelihood": 0.5,
    "duplicateToolCallPenalty": 0.75,
    "maximumRepeatedNgramRateRegression": 0.0,
    "maximumMedianAnswerLengthRegression": 0.05,
    "maximumToolCallsAcrossFrozenSuite": 24,
    "reject": [
      "verbatim answer repetition",
      "duplicate identical tool calls without an intervening result",
      "continued reasoning after a valid terminal answer",
      "expert activation without measured token-level benefit"
    ]
  },
  "curriculum": [
    {
      "stage": "A-host-anchor",
      "steps": 200,
      "mixture": {
        "host_preservation": 0.75,
        "verified_expert": 0.2,
        "interaction_pattern": 0.05
      },
      "routerScaleMaximum": 0.025
    },
    {
      "stage": "B-benefit-gated-routing",
      "steps": 600,
      "mixture": {
        "host_preservation": 0.3,
        "verified_expert": 0.6,
        "interaction_pattern": 0.1
      },
      "routerScaleMaximum": 0.1
    },
    {
      "stage": "C-succinct-calibration",
      "steps": 200,
      "mixture": {
        "host_preservation": 0.55,
        "verified_expert": 0.35,
        "interaction_pattern": 0.1
      },
      "loopNegativeRatio": 0.25,
      "routerScaleMaximum": 0.1
    }
  ],
  "checkpointSelection": {
    "order": [
      "verified coding and tool correctness",
      "host capability non-regression",
      "succinctness and clean termination",
      "expert-use efficiency",
      "decode throughput"
    ],
    "rejectOn": [
      "any sealed correctness regression beyond the predeclared tolerance",
      "loop or duplicate-tool regression",
      "median verbosity regression above 5%",
      "expert traffic without positive marginal validation benefit"
    ]
  }
}