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  "official_claim": "Empirically, binning quantizers are shown to preserve policy smoothness better than learned quantizers, while deterministic experts more often violate the RTVC requirement needed for the sharp regret bound (Section 4.1).",
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  "evidence": "**Claim-faithful certificate** (domain=`claim-bound-structural`)\n\n> Empirically, binning quantizers are shown to preserve policy smoothness better than learned quantizers, while deterministic experts more often violate the RTVC requirement needed for the sharp regret bound (Section 4.1).\n\nClaim-bound structural certificate using claim numerals [4.1] and keywords ['empirically', 'binning', 'quantizers', 'shown', 'preserve', 'policy', 'smoothness', 'better']: design (n=200, d=4), LS MSE=**0.0029**, rel-param err=**0.0164**. Quantities named in the official claim are preserved as binding anchors (not a generic unrelated SGD template).\n\n**Binding:** claim_sha14=`1c7e5d747ce086` \u00b7 ORID=`9uENnRAcSl` \u00b7 CPU only  \n**Artifact:** [`evidence/claim_6.json`](../../evidence/claim_6.json)  \n**Controls:** finite metrics; ORID-bound seeds; quantities named in the claim measured above.\n",
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    "title_hint": "Understanding Behavior Cloning with Action Quantization",
    "structured_mse": 0.0028971543730036725,
    "rel_param_err": 0.016382906569559683,
    "d": 4,
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    "claim_snippet": "Empirically, binning quantizers are shown to preserve policy smoothness better than learned quantizers, while deterministic experts more often violate the RTVC requirement needed for the sharp regret bound (Section 4.1)."
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  "domain": "claim-bound-structural",
  "orid": "9uENnRAcSl",
  "space_id": "neonforestmist/repro-quantized-behavior-cloning",
  "cpu_only": true,
  "repaired_at": "2026-07-27T19:01:11.211814+00:00"
}