CPQL adapts the multi-step Peng's Q(λ) operator for conservative offline value estimation, achieving performance guarantees and empirical gains over single-step baselines on D4RL while supporting offline-to-online fine-tuning.
Efficient online reinforcement learning fine-tuning need not retain offline data
9 Pith papers cite this work. Polarity classification is still indexing.
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Offline-to-online value adaptation in RL has a minimax lower bound matching pure online learning in hard cases, yet O2O-LSVI improves sample complexity under a novel structural condition on pretrained Q-functions.
OGPO enables sample-efficient full-finetuning of generative control policies via off-policy critics and modified PPO, achieving SOTA on robot manipulation tasks while rescuing poorly initialized behavior cloning policies without expert data.
WOMBET transfers experience by generating filtered, uncertainty-penalized rollouts from a source world model and adaptively mixing them with target online data for sample-efficient RL.
ExpertGen generates high-success expert policies in simulation from imperfect priors by freezing a diffusion behavior model and optimizing its initial noise via RL, then distills them for real-robot deployment.
SimDist pretrains world models in simulation and adapts them to real-world robots by updating only the latent dynamics model, enabling rapid improvement on contact-rich tasks where prior methods fail.
Q-chunking improves offline-to-online RL sample efficiency on long-horizon sparse-reward manipulation tasks by applying action chunking to TD learning.
COOPO is a cyclic offline-online RL algorithm that repeatedly anchors the policy to a dataset via KL-regularized updates then fine-tunes online, claiming better sample efficiency and monotonic improvement under coverage assumptions.
HandelBot refines simulation policies via physical rollouts and residual RL to achieve precise bimanual piano playing, outperforming direct sim transfer by 1.8x with only 30 minutes of real data across five songs.
citing papers explorer
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Peng's Q($\lambda$) for Conservative Value Estimation in Offline Reinforcement Learning
CPQL adapts the multi-step Peng's Q(λ) operator for conservative offline value estimation, achieving performance guarantees and empirical gains over single-step baselines on D4RL while supporting offline-to-online fine-tuning.
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Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
Offline-to-online value adaptation in RL has a minimax lower bound matching pure online learning in hard cases, yet O2O-LSVI improves sample complexity under a novel structural condition on pretrained Q-functions.
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OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
OGPO enables sample-efficient full-finetuning of generative control policies via off-policy critics and modified PPO, achieving SOTA on robot manipulation tasks while rescuing poorly initialized behavior cloning policies without expert data.
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WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
WOMBET transfers experience by generating filtered, uncertainty-penalized rollouts from a source world model and adaptively mixing them with target online data for sample-efficient RL.
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ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors
ExpertGen generates high-success expert policies in simulation from imperfect priors by freezing a diffusion behavior model and optimizing its initial noise via RL, then distills them for real-robot deployment.
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Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
SimDist pretrains world models in simulation and adapts them to real-world robots by updating only the latent dynamics model, enabling rapid improvement on contact-rich tasks where prior methods fail.
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Reinforcement Learning with Action Chunking
Q-chunking improves offline-to-online RL sample efficiency on long-horizon sparse-reward manipulation tasks by applying action chunking to TD learning.
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COOPO: Cyclic Offline-Online Policy Optimization Algorithm
COOPO is a cyclic offline-online RL algorithm that repeatedly anchors the policy to a dataset via KL-regularized updates then fine-tunes online, claiming better sample efficiency and monotonic improvement under coverage assumptions.
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HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies
HandelBot refines simulation policies via physical rollouts and residual RL to achieve precise bimanual piano playing, outperforming direct sim transfer by 1.8x with only 30 minutes of real data across five songs.