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ARXIV:2604.19730 · REINFORCEMENT LEARNING · SUBMITTED 22 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.19730REINFORCEMENT LEARNINGSUBMITTED 22 APR · 20:32 UTCFRESHNESS STALEPerry Dong · Alexander Swerdlow · Dorsa Sadigh · Chelsea Finn · arXiv
FASTER enables sampling-based test-time scaling for diffusion-based policies without the computational cost by tracing performance gains earlier in the denoising process.
Opportunity summary
Pain FASTER enables sampling-based test-time scaling for diffusion-based policies without the computational cost by tracing performance gains earlier in the denoising process.
Evidence 0 refs | 4 sources | 83% coverage
Blocker Evidence partial
FASTER enables sampling-based test-time scaling for diffusion-based policies without the computational cost by tracing performance gains earlier in the denoising process. In this work, we propose FASTER, a method for getting the benefits of…
Some of the most performant reinforcement learning algorithms today can be prohibitively expensive as they use test-time scaling methods such as sampling multiple action candidates and selecting the best one. In this work, we…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. The result is a method that is lightweight and can be plugged into existing generative RL algorithms. A public repository is linked, so build…
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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FASTER enables sampling-based test-time scaling for diffusion-based policies without the computational cost by tracing performance gains earlier in the denoising process.
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Paper Pack
10.48550/arXiv.2604.19730FASTER enables sampling-based test-time scaling for diffusion-based policies without the computational cost by tracing performance gains earlier in the denoising process.
Abstract
Some of the most performant reinforcement learning algorithms today can be prohibitively expensive as they use test-time scaling methods such as sampling multiple action candidates and selecting the best one. In this work, we propose FASTER, a method for getting the benefits of sampling-based test-time scaling of diffusion-based policies without the computational cost by tracing the performance gain of action samples back to earlier in the denoising process. Our key insight is that we can model the denoising of multiple action candidates and selecting the best one as a Markov Decision Process (MDP) where the goal is to progressively filter action candidates before denoising is complete. With this MDP, we can learn a policy and value function in the denoising space that predicts the downstream value of action candidates in the denoising process and filters them while maximizing returns. The result is a method that is lightweight and can be plugged into existing generative RL algorithms. Across challenging long-horizon manipulation tasks in online and batch-online RL, FASTER consistently improves the underlying policies and achieves the best overall performance among the compared methods. Applied to a pretrained VLA, FASTER achieves the same performance while substantially reducing training and inference compute requirements. Code is available at https://github.com/alexanderswerdlow/faster .
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Proof status
partial0 refs; 4 sources; 83% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 8.0
PROBLEM
FASTER enables sampling-based test-time scaling for diffusion-based policies without the computational cost by tracing performance gains earlier in the denoising process. In this work, we propose FASTER, a method for getting the benefits of sampling-based test-time scaling of di...
METHOD
Some of the most performant reinforcement learning algorithms today can be prohibitively expensive as they use test-time scaling methods such as sampling multiple action candidates and selecting the best one. In this work, we propose FASTER, a method for getting the benefits of...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. The result is a method that is lightweight and can be plugged into existing generative RL algorithms. A public repository is linked, so build verification can inspect implementation evidence instead of tr...
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 16, "author": "Perry Dong; Alexander Swerdlow; Dorsa Sadigh; Chelsea Finn", "title": "FASTER: Value-Guided Sampling for Fast RL", "creation date": null
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Concepts
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FASTER enables sampling-based test-time scaling for diffusion-based policies without the computational cost by tracing performance gains earlier in the denoising process.
Segment
Reinforcement Learning
Adoption evidence
Public code linked for build inspection
Commercial read
8.0/10 public viability
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Build Passport
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status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
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passport absent
stale
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Technical feasibility
partial
Current read
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Gaps
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Evidence
0 references, 4 sources, 83% evidence coverage.
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Buyer clarity
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Evidence
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Write integration checklist from prototype path and target workflow.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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