LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL
Abstract
While offline reinforcement learning (RL) enables policy optimization from static datasets without costly online interaction, it remains bottlenecked by the risk of executing out-of-distribution (OOD) actions. Recent approaches mitigate this by learning a behavior-cloning policy through flow matching and then performing RL within its constrained latent space. However, naively optimizing the latent policy can easily cause the policy to collapse into a brittle mode or exploit sharp artifacts of the learned critic. In this work, we find that entropy regularization is essential in latent-space RL for addressing these challenges. We introduce LASER, a novel offline RL algorithm that applies latent-space adjoint matching to achieve entropy-regularized latent-space RL with expressive flow policies while avoiding backpropagation through time. Through comprehensive experiments on 40 challenging OGBench tasks with varying dataset qualities, we show that LASER achieves state-of-the-art performance. Notably, LASER uses fixed method-specific hyperparameters across all tasks and outperforms the evaluated baselines, including those with task- and dataset-specific tuning, which highlights the robust applicability of LASER. Project website: https://mit-realm.github.io/laser/.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
LASER introduces a robust offline RL algorithm that uses latent-space adjoint matching to achieve entropy-regularized policy optimization with flow-based policies, effectively decoupling support constraints from policy extraction stability. The paper makes a significant contribution to the field by identifying entropy regularization as a critical, previously under-exploited component in support-constrained latent-space RL, and by providing an efficient, BPTT-free optimization method that yields state-of-the-art results on the OGBench benchmark with superior hyperparameter robustness.
Methodology
The paper proposes LASER, a framework for offline RL that combines support-constrained latent space policies with explicit entropy regularization. The core methodological contribution is the use of "adjoint matching" to optimize the entropy-regularized policy objective in latent space without backpropagation through time (BPTT). This is a significant technical advancement over previous flow-based offline RL methods (like ReFORM or DSRL) which either relied on implicit regularization or computationally expensive BPTT. The derivation of the optimal Q-tilted distribution and its implementation via stochastic optimal control (SOC) is mathematically sound and provides a clear path for stable training. The separation of support constraints (handled by the flow decoder) from policy extraction stability (handled by entropy regularization) is a well-motivated architectural choice.
Results & evaluation
The experiments are conducted on the OGBench benchmark, covering 40 tasks across locomotion and manipulation. The results show that LASER achieves state-of-the-art performance, particularly on noisy datasets where support constraints are critical. A key strength is the demonstration of robustness: LASER uses a fixed set of hyperparameters across all tasks, outperforming baselines that require task-specific tuning. The ablation studies effectively isolate the contribution of entropy regularization and the efficiency of adjoint matching vs. BPTT (showing a 3.6x speedup). The comparison against recent flow-based baselines (IFQL, FQL, QAM) is appropriate and rigorous.
Reproducibility
The paper provides a clear algorithmic summary and references a project website. The use of standard benchmarks (OGBench) and fixed hyperparameters enhances reproducibility. However, the specific implementation details of the adjoint matching solver and the flow matching training are deferred to the appendix, which is standard but requires careful reading. The code availability is implied by the project URL.
Limitations
The method relies on the quality of the behavior cloning (BC) decoder to enforce support constraints; if the decoder is imperfect, OOD actions can still occur. The computational cost of solving ODEs during training remains high, though mitigated by adjoint matching. The paper acknowledges that the value function learning is relatively simple compared to more advanced pessimistic methods, suggesting room for improvement in the critic component.
Broader impact
This work advances the state of the art in safe offline RL, which is critical for deploying RL agents in real-world robotic systems where online interaction is costly or dangerous. The technique of using adjoint matching for entropy-regularized flow policies could be adapted to other generative model-based RL settings, potentially influencing how expressive policies are trained in broader RL research. LASER introduces a robust offline RL algorithm that uses latent-space adjoint matching to achieve entropy-regularized policy optimization with flow-based policies, effectively decoupling support constraints from policy extraction stability. The paper makes a significant contribution to the field by identifying entropy regularization as a critical, previously under-exploited component in support-constrained latent-space RL, and by providing an efficient, BPTT-free optimization method that yields state-of-the-art results on the OGBench benchmark with superior hyperparameter robustness.
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