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ARXIV:2605.14497 · REINFORCEMENT LEARNING · SUBMITTED 15 MAY · 20:12 UTC · FRESHNESS FRESH
ARXIV:2605.14497REINFORCEMENT LEARNINGSUBMITTED 15 MAY · 20:12 UTCFRESHNESS FRESHLetian Yang · Xu Liu · Yiqiang Lu · Jian Liu · Weiqiang Wang · Shuai Li · arXiv
A plug-and-play framework for adaptive data mixing in offline-to-online reinforcement learning, improving stability and performance.
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Pain A plug-and-play framework for adaptive data mixing in offline-to-online reinforcement learning, improving stability and performance.
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A plug-and-play framework for adaptive data mixing in offline-to-online reinforcement learning, improving stability and performance. A key challenge lies in the non-stationary distribution shift between offline datasets and the evolving online policy.
Offline-to-online reinforcement learning harnesses the stability of offline pretraining and the flexibility of online fine-tuning. A key challenge lies in the non-stationary distribution shift between offline datasets and the evolving online policy.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our empirical results demonstrate that this approach consistently outperforms existing data replay methods across various datasets, eliminating the need for manual, context-specific adjustments while…
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A plug-and-play framework for adaptive data mixing in offline-to-online reinforcement learning, improving stability and performance.
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10.48550/arXiv.2605.14497A plug-and-play framework for adaptive data mixing in offline-to-online reinforcement learning, improving stability and performance.
Abstract
Offline-to-online reinforcement learning harnesses the stability of offline pretraining and the flexibility of online fine-tuning. A key challenge lies in the non-stationary distribution shift between offline datasets and the evolving online policy. Common approaches often rely on static mixing ratios or heuristic-based replay strategies, which lack adaptability to different environments and varying training dynamics, resulting in suboptimal tradeoff between stability and asymptotic performance. In this work, we propose Reinforcement Learning with Optimized Adaptive Data-mixing (ROAD), a dynamic plug-and-play framework that automates the data replay process. We identify a fundamental objective misalignment in existing approaches. To tackle this, we formulate the data selection problem as a bi-level optimization process, interpreting the data mixing strategy as a meta-decision governing the policy performance (outer-level) during online fine-tuning, while the conventional Q-learning updates operate at the inner level. To make it tractable, we propose a practical algorithm using a multi-armed bandit mechanism. This is guided by a surrogate objective approximating the bi-level gradient, which simultaneously maintains offline priors and prevents value overestimation. Our empirical results demonstrate that this approach consistently outperforms existing data replay methods across various datasets, eliminating the need for manual, context-specific adjustments while achieving superior stability and asymptotic performance.
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PROBLEM
A plug-and-play framework for adaptive data mixing in offline-to-online reinforcement learning, improving stability and performance. A key challenge lies in the non-stationary distribution shift between offline datasets and the evolving online policy.
METHOD
Offline-to-online reinforcement learning harnesses the stability of offline pretraining and the flexibility of online fine-tuning. A key challenge lies in the non-stationary distribution shift between offline datasets and the evolving online policy.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our empirical results demonstrate that this approach consistently outperforms existing data replay methods across various datasets, eliminating the need for manual, context-specific adjustments while achi...
WHY NOW
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A plug-and-play framework for adaptive data mixing in offline-to-online reinforcement learning, improving stability and performance. A key challenge lies in the non-stationary distribution shift between offline datasets and the evolving online policy.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Offline-to-online reinforcement learning harnesses the stability of offline pretraining and the flexibility of online fine-tuning. A key challenge lies in the non-stationary distribution shift between offline datasets and the evolving online policy.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our empirical results demonstrate that this approach consistently outperforms existing data replay methods across various datasets, eliminating the need for manual, context-specific adjustments while achieving superior stability and asymptotic performance. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A plug-and-play framework for adaptive data mixing in offline-to-online reinforcement learning, improving stability and performance.
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