LASER is a new offline reinforcement learning algorithm that performs latent-space adjoint matching with entropy regularization to avoid policy collapse and critic exploitation. It uses expressive flow policies without backpropagation through time and applies a single set of hyperparameters across all tasks. Experiments on 40 OGBench tasks show LASER outperforms baselines, achieving state‑of‑the‑art results with robust, task‑agnostic performance.
arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.
arXiv:2107. 08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures.
The paper introduces fixed‑point flow matching (FFM), a data‑free method for training and fine‑tuning flow policies in reinforcement learning. FFM formulates the actor update as a fixed‑point iteration using a flow‑matching regression objective, where the critic’s action gradients act as targets. Experiments show that FFM can effectively fine‑tune pretrained flow policies in offline‑to‑online scenarios and train flow policies from scratch in online RL, establishing it as a principled approach for learning expressive flow‑based policies without training data.
arXiv:2603. 25464v2 Announce Type: replace-cross Abstract: Zero-shot reinforcement learning (RL) algorithms aim to learn a family of policies from a reward-free dataset, and recover optimal policies for any reward function directly at test time.
The paper introduces Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints (FMER), a reinforcement learning framework that uses ODE-based flow matching to overcome limitations of SDE-based diffusion policies. FMER derives an analytic entropy objective to correct density distortion from a tanh transformation, establishes that minimizing an advantage-weighted conditional flow matching loss serves as a simulation-free surrogate for policy mirror descent, and dynamically tunes the mirror descent temperature to enforce a robust trust region. Experiments show FMER outperforms baselines on the sparse-reward FrankaKitchen task while remaining competitive on dense-reward MuJoCo benchmarks.
The paper introduces AnchorQ, a method that improves sample-efficient reinforcement learning for policy refinement by using flow-reversed action anchors. AnchorQ maps reference behaviors to state-conditioned latent anchors, samples local particles around these anchors, and employs value-guided sampling to explore promising refinements. Experiments on simulated and real-world manipulation tasks show that AnchorQ outperforms state-of-the-art post-training baselines, achieving a 37% higher real-world success rate and 97% greater simulated task throughput within the same interaction budget.
arXiv:2601. 23075v2 Announce Type: replace Abstract: On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies, often leading to brittle optimization when gradients are noisy, and policy updates must be conservative.
arXiv:2605.12379v2 Announce Type: replace-cross Abstract: Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching a...
The paper proposes a new loss scheme that simultaneously supervises zeroth- and first-order actions to improve policy performance and control robustness in data-driven decision-making methods such as imitation learning and reinforcement learning. The approach provides formal theoretical guarantees, works with any off-the-shelf policy model, and can be added as a lightweight plug‑and‑play module to existing offline RL frameworks. Experiments on OGBench and D4RL show significant gains in performance, robustness, and out‑of‑distribution generalization, especially in low‑data settings.
arXiv:2608. 01205v1 Announce Type: new Abstract: Recent offline reinforcement learning methods increasingly rely on expressive generative policies and specialized value-guidance mechanisms.
The paper introduces LAC (Light Actor, deep Critic), an offline reinforcement learning approach that allocates model capacity to a deep critic rather than a complex actor to improve inference efficiency. It addresses three failure modes—optimization, bootstrap-noise amplification, and value-range drift—using a residual MLP backbone, n‑step bootstrap targets, and a categorical cross‑entropy loss. Experiments on OGBench show LAC matches state‑of‑the‑art diffusion and flow‑matching baselines while reducing inference latency by up to four times.
arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...