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ARXIV:2605.13054 · REINFORCEMENT LEARNING · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13054REINFORCEMENT LEARNINGSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHMinung Kim · Jeongmo Kim · Gwanwoo Choi · Seungyul Han · arXiv
A framework for adapting reinforcement learning policies to new environments using synthesized data.
Opportunity summary
Pain A framework for adapting reinforcement learning policies to new environments using synthesized data.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A framework for adapting reinforcement learning policies to new environments using synthesized data. A key challenge is to leverage source data while reducing distributional mismatch, particularly when the target dataset is extremely limited.
Cross-domain offline reinforcement learning aims to adapt a policy from a source domain to a target domain using only pre-collected datasets, where environment dynamics may differ. A key challenge is to leverage source data…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Extensive experiments across diverse cross-domain environments show that TCE consistently outperforms state-of-the-art cross-domain offline RL baselines. Code availability is flagged in the production record;…
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.
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A framework for adapting reinforcement learning policies to new environments using synthesized data.
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Paper Pack
10.48550/arXiv.2605.13054A framework for adapting reinforcement learning policies to new environments using synthesized data.
Abstract
Cross-domain offline reinforcement learning aims to adapt a policy from a source domain to a target domain using only pre-collected datasets, where environment dynamics may differ. A key challenge is to leverage source data while reducing distributional mismatch, particularly when the target dataset is extremely limited. To address this, we propose Target-aligned Coverage Expansion (TCE), a framework that decides how source data should be used, either by directly incorporating target-near transitions or by expanding state coverage through target-aligned generation, guided by theoretical analysis. TCE builds on a dual score-based generative model to synthesize target-consistent transitions over an expanded state region. Extensive experiments across diverse cross-domain environments show that TCE consistently outperforms state-of-the-art cross-domain offline RL baselines.
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PROBLEM
A framework for adapting reinforcement learning policies to new environments using synthesized data. A key challenge is to leverage source data while reducing distributional mismatch, particularly when the target dataset is extremely limited.
METHOD
Cross-domain offline reinforcement learning aims to adapt a policy from a source domain to a target domain using only pre-collected datasets, where environment dynamics may differ. A key challenge is to leverage source data while reducing distributional mismatch, particularly wh...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Extensive experiments across diverse cross-domain environments show that TCE consistently outperforms state-of-the-art cross-domain offline RL baselines. Code availability is flagged in the production rec...
WHY NOW
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for adapting reinforcement learning policies to new environments using synthesized data. A key challenge is to leverage source data while reducing distributional mismatch, particularly when the target dataset is extremely limited.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Cross-domain offline reinforcement learning aims to adapt a policy from a source domain to a target domain using only pre-collected datasets, where environment dynamics may differ. A key challenge is to leverage source data while reducing distributional mismatch, particularly when the target dataset is extremely limited.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Extensive experiments across diverse cross-domain environments show that TCE consistently outperforms state-of-the-art cross-domain offline RL baselines. 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 3.0/10. 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 framework for adapting reinforcement learning policies to new environments using synthesized data.
Segment
Reinforcement Learning
Adoption evidence
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Commercial read
3.0/10 public viability
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Technical feasibility
partial
Current read
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Run minimal reproduction from the Build Passport prototype path.
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Current read
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