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ARXIV:2602.05051 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.05051REINFORCEMENT LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
ReFORM optimizes flow policies to enhance offline RL by reducing out-of-distribution errors and maximizing policy performance.
Opportunity summary
Pain ReFORM optimizes flow policies to enhance offline RL by reducing out-of-distribution errors and maximizing policy performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
ReFORM optimizes flow policies to enhance offline RL by reducing out-of-distribution errors and maximizing policy performance. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy…
Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. One common challenge that arises in this setting is the out-of-distribution (OOD)…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We propose ReFORM, an offline RL method based on flow policies that enforces the less restrictive support constraint by construction.
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
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ReFORM optimizes flow policies to enhance offline RL by reducing out-of-distribution errors and maximizing policy performance.
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10.48550/arXiv.2602.05051ReFORM optimizes flow policies to enhance offline RL by reducing out-of-distribution errors and maximizing policy performance.
Abstract
Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy leaves the training distribution. Prior methods penalize a statistical distance term to keep the policy close to the behavior policy, but this constrains policy improvement and may not completely prevent OOD actions. Another challenge is that the optimal policy distribution can be multimodal and difficult to represent. Recent works apply diffusion or flow policies to address this problem, but it is unclear how to avoid OOD errors while retaining policy expressiveness. We propose ReFORM, an offline RL method based on flow policies that enforces the less restrictive support constraint by construction. ReFORM learns a behavior cloning (BC) flow policy with a bounded source distribution to capture the support of the action distribution, then optimizes a reflected flow that generates bounded noise for the BC flow while keeping the support, to maximize the performance. Across 40 challenging tasks from the OGBench benchmark with datasets of varying quality and using a constant set of hyperparameters for all tasks, ReFORM dominates all baselines with hand-tuned hyperparameters on the performance profile curves.
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PROBLEM
ReFORM optimizes flow policies to enhance offline RL by reducing out-of-distribution errors and maximizing policy performance. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy leaves the training distribution.
METHOD
Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the po...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We propose ReFORM, an offline RL method based on flow policies that enforces the less restrictive support constraint by construction.
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
ReFORM optimizes flow policies to enhance offline RL by reducing out-of-distribution errors and maximizing policy performance. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy leaves the training distribution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy leaves the training distribution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We propose ReFORM, an offline RL method based on flow policies that enforces the less restrictive support constraint by construction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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ReFORM optimizes flow policies to enhance offline RL by reducing out-of-distribution errors and maximizing policy performance.
Segment
Reinforcement Learning
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5.0/10 public viability
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