| { |
| "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" |
| ] |
| } |
| } |
|
|
