Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback

Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/corruption-robust-offline-multi-agent-reinforcement-learning-from-human-feedback

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 28
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID corruption-robust-offline-multi-agent-reinforcement-learning-from-human-feedback | Route /signal-canvas/corruption-robust-offline-multi-agent-reinforcement-learning-from-human-feedback

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Evidence Receipt

Route status: building

Claims: 8

References: 28

Proof: Verification pending

Freshness state: computing

Source paper: Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback

PDF: https://arxiv.org/pdf/2603.28281v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-31T20:24:05.016Z

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Not build-ready: Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback

/buildability/corruption-robust-offline-multi-agent-reinforcement-learning-from-human-feedback

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Subject: Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
First, under a uniform coverage assumption - where every policy of interest is sufficiently represented in the clean (prior to corruption) data - we introduce a robust estimator that guarantees an O(ε^{1 - o(1)}) bound on the Nash equilibrium gap.
Implicationpartial
Directly stated in the abstract with supporting theoretical results in the analysis.
Verificationpartial
partial
Evidencepartial
In this case, our proposed algorithm achieves an O(√ε) bound on the Nash gap.
Implicationpartial
Directly stated in the abstract and supported by the results table.
Verificationpartial
partial
Evidencepartial
Both of these procedures, however, suffer from intractable computation.
Implicationpartial
Explicitly stated in the abstract as a limitation of the proposed methods.
Verificationpartial
partial
Evidencepartial
Under the same unilateral coverage regime, we derive a quasi-polynomial-time algorithm whose CCE gap scales as O(√ε).
Implicationpartial
Directly stated in the abstract and supported by the results table.
Verificationpartial
partial
Evidencepartial
To the best of our knowledge, this is the first systematic treatment of adversarial data corruption in offline MARLHF.
Implicationpartial
Explicitly claimed as a novel contribution in the abstract.
Verificationpartial
partial
Evidencepartial
We model the problem using the framework of linear Markov games. First, under a uniform coverage assumption...
Implicationpartial
Directly stated in the abstract as the core problem formulation.
Verificationpartial
partial
Evidencepartial
This dependence is identical to that in single-agent RL [Zhang et al., 2022], two-player zero-sum Markov games [Nika et al., 2024b], and single-agent RLHF [Mandal et al., 2025] under data corruption.
Implicationpartial
Explicitly stated in the analysis with comparison to prior work.
Verificationpartial
partial
Evidencepartial
Unilateral coverage is in fact necessary and sufficient to provide any meaningful guarantees in zero-sum [Zhong et al., 2022] and general-sum Markov games [Zhang et al., 2023].
Implicationpartial
Strongly supported by theoretical discussion and references to prior work.
Verificationpartial
partial

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Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback

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