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pith:3W7OS5CB

pith:2025:3W7OS5CBNYZQIEXVYDM2XADOXF
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From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning

Lipeng Zu, Shayok Chakraborty, Xiaonan Zhang, Yu Qian

DARE releases constraints at the sample level in offline-to-online reinforcement learning by measuring behavioral consistency instead of data origin.

arxiv:2511.03828 v3 · 2025-11-05 · cs.LG

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3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

We provide a theoretical analysis showing that behavior-based sample exchange consistently improves the distinction between offline-like and online-like subsets. DARE is the first to condition constraint release on behavioral consistency via a posterior-induced exchange mechanism, moving beyond a binary offline/online data distinction.

C2weakest assumption

The assumption that a learned behavior model can reliably produce a posterior that accurately measures per-sample behavioral consistency for the exchange mechanism, and that this consistency metric remains meaningful as the policy evolves during fine-tuning (abstract, paragraph on DARE framework).

C3one line summary

DARE provides a distribution-aware sample-level constraint release mechanism for offline-to-online RL based on behavioral consistency with a behavior model, supported by theoretical analysis and D4RL experiments showing improved stability and performance.

Formal links

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Receipt and verification
First computed 2026-05-20T00:04:16.798543Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

ddbee974416e330412f5c0d9ab806eb9417a0dc3b6ddad55bbb0bd2b32113879

Aliases

arxiv: 2511.03828 · arxiv_version: 2511.03828v3 · doi: 10.48550/arxiv.2511.03828 · pith_short_12: 3W7OS5CBNYZQ · pith_short_16: 3W7OS5CBNYZQIEXV · pith_short_8: 3W7OS5CB
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3W7OS5CBNYZQIEXVYDM2XADOXF \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ddbee974416e330412f5c0d9ab806eb9417a0dc3b6ddad55bbb0bd2b32113879
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-11-05T19:48:46Z",
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