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ARXIV:2604.22229 · OFFLINE REINFORCEMENT LEARNING · SUBMITTED 27 APR · 20:15 UTC · FRESHNESS STALE
ARXIV:2604.22229OFFLINE REINFORCEMENT LEARNINGSUBMITTED 27 APR · 20:15 UTCFRESHNESS STALEZhancun Mu · Guangyu Zhao · Yiwu Zhong · Chi Zhang · arXiv
DROL is a one-step offline RL actor using dynamic routing to improve performance by allowing candidate actions to dynamically claim supported regions of the dataset.
Opportunity summary
Pain DROL is a one-step offline RL actor using dynamic routing to improve performance by allowing candidate actions to dynamically claim supported regions of the dataset.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
DROL is a one-step offline RL actor using dynamic routing to improve performance by allowing candidate actions to dynamically claim supported regions of the dataset. In recent one-step extraction pipelines, a strong iterative teacher…
One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic without drifting away from actions that the…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve…
Offline Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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DROL is a one-step offline RL actor using dynamic routing to improve performance by allowing candidate actions to dynamically claim supported regions of the dataset.
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Paper Pack
10.48550/arXiv.2604.22229DROL is a one-step offline RL actor using dynamic routing to improve performance by allowing candidate actions to dynamically claim supported regions of the dataset.
Abstract
One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic without drifting away from actions that the dataset can support. In recent one-step extraction pipelines, a strong iterative teacher provides one target action for each latent draw, and the same student output is asked to do both jobs: move toward higher Q and stay near that paired endpoint. If those two directions disagree, the loss resolves them as a compromise on that same sample, even when a nearby better action remains locally supported by the data. We propose DROL, a latent-conditioned one-step actor trained with top-1 dynamic routing. For each state, the actor samples $K$ candidate actions from a bounded latent prior, assigns each dataset action to its nearest candidate, and updates only that winner with Behavior Cloning and critic guidance. Because the routing is recomputed from the current candidate geometry, ownership of a supported region can shift across candidates over the course of learning. This gives a one-step actor room to make local improvements that pointwise extraction struggles to capture, while retaining single-pass inference at test time. On OGBench and D4RL, DROL is competitive with the one-step FQL baseline, improving many OGBench task groups while remaining strong on both AntMaze and Adroit. Project page: https://muzhancun.github.io/preprints/DROL.
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PROBLEM
DROL is a one-step offline RL actor using dynamic routing to improve performance by allowing candidate actions to dynamically claim supported regions of the dataset. In recent one-step extraction pipelines, a strong iterative teacher provides one target action for each latent dr...
METHOD
One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic without drifting away from actions that the dataset can support. In recent one-step extraction pip...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic without drifting away f...
WHY NOW
Offline Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
DROL is a one-step offline RL actor using dynamic routing to improve performance by allowing candidate actions to dynamically claim supported regions of the dataset. In recent one-step extraction pipelines, a strong iterative teacher provides one target action for each latent draw, and the same student output is asked to do both jobs: move toward higher Q and stay near that paired endpoint.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic without drifting away from actions that the dataset can support. In recent one-step extraction pipelines, a strong iterative teacher provides one target action for each latent draw, and the same student output is asked to do both jobs: move toward higher Q and stay near that paired endpoint.
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. One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic without drifting away from actions that the dataset can support. 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
Offline Reinforcement Learning moved forward this cycle; last verified April 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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DROL is a one-step offline RL actor using dynamic routing to improve performance by allowing candidate actions to dynamically claim supported regions of the dataset.
Segment
Offline Reinforcement Learning
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Commercial read
7.0/10 public viability
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