AdaScope adaptively selects optimal RL intervention points during diffusion denoising by monitoring structural and semantic changes, delivering 66% higher performance at 59% lower cost than full-trajectory RL baselines.
Integrating behavior cloning and reinforcement learning for improved performance in dense and sparse reward environments
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
DF-ExpEnse improves sample efficiency in finetuning diffusion-based robotic policies by filtering diffusion-generated actions with critic ensembles and enabling fleet-level collaboration.
TacCoRL integrates tactile feedback into VLA policies via real-aligned simulation co-training and RL, raising average success from 50% to 72.5% on four bimanual contact-rich tasks with direct real-robot transfer.
citing papers explorer
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Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models?
AdaScope adaptively selects optimal RL intervention points during diffusion denoising by monitoring structural and semantic changes, delivering 66% higher performance at 59% lower cost than full-trajectory RL baselines.
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DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
DF-ExpEnse improves sample efficiency in finetuning diffusion-based robotic policies by filtering diffusion-generated actions with critic ensembles and enabling fleet-level collaboration.
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TacCoRL: Integrating Tactile Feedback into VLA via Simulation
TacCoRL integrates tactile feedback into VLA policies via real-aligned simulation co-training and RL, raising average success from 50% to 72.5% on four bimanual contact-rich tasks with direct real-robot transfer.