Representation alignment (REPA) is widely used to accelerate the training of latent-space diffusion transformers (DiTs). However, applying it to pixel-space models such as Just Image Transformers (JiT) is challenging. Our analysis reveals a pronounced representation conflict between discriminative alignment and pixel-space generation. Compared with its latent counterpart, JiT learns representations that are substantially less aligned with DINOv2. Applying REPA to JiT consistently reduces the reconstructability of its intermediate representations, whereas this effect is not observed in Latent JiT. We attribute this conflict to JiT directly reconstructing pixels, which requires its representations to hierarchically encode discriminative semantics and reconstruction-relevant visual information across network depths. To address these challenges, we propose Routed Representation Alignment (RouteREPA), which uses lightweight, representation-dependent routers to adaptively route task-oriented representations for alignment and generation. We further apply routing across multiple network depths to accommodate their depth-dependent representation requirements. Extensive experiments on ImageNet-1K at 256x256 resolution show that RouteREPA consistently improves JiT across model scales and training budgets, outperforming standard REPA. On JiT-H/16, RouteREPA further surpasses PixelREPA using only one-third of its training epochs without classifier-free guidance.
Representation regularization has emerged as an effective way to improve diffusion transformer training. Yet one of the most canonical regularizers--InfoNCE-style contrastive learning--remains largely underexplored in this setting. We naturally ask a question: can standard contrastive learning serve as an effective regularizer for diffusion transformers? Surprisingly, we find that naive InfoNCE regularization brings only marginal gains. Our analysis reveals that the objective is easily dominated by a shortcut: near-orthogonal noise components in diffusion inputs provide a bypass for enlarging negative-pair distances while weakening positive-pair alignment, especially at high noise levels. To unlock the benefit of contrastive regularization, we propose Clean Representation-ANchored Contrastive (CRAN) regularization. Specifically, CRAN anchors contrastive dispersion with clean-sample representation distances, transferring clean relational semantic structure to noisy diffusion representations to suppress the noise shortcut. Extensive experiments on ImageNet show that CRAN consistently improves diffusion transformer training across model scales and training budgets, outperforming internal feature-alignment and dispersive regularization baselines while matching external DINO-guided REPA. These results re-establish InfoNCE-based contrastive learning as an effective regularizer for diffusion training, once its noise-driven over-dispersion is properly constrained.
Actor-critic methods are central to modern reinforcement learning, yet off-policy actor-critic theory remains far removed from practical algorithms, due in part to the interaction between the deadly triad and the continually changing target policy. We revisit functional critics, i.e., value functions that explicitly condition on the policy, and show that this representation provides a shared foundation for off-policy training stability and policy-space exploration. We first develop a convergent off-policy actor-critic framework under linear function approximation that accommodates target-based TD, partial coverage, and evolving behavior policies. Our analysis combines a policy-evaluation guarantee under partial coverage with policy-gradient generalization of functional critics to yield a dual trust-coverage framework that controls policy-gradient error under evolving behavior policies while enabling greater reuse of off-policy data. We further develop a replay-buffer analysis under i.i.d. sampling, yielding a principled replay-sampling design for minimizing the replay-sample complexity required to reach a prescribed evaluation accuracy. We next show that functional critics provide a natural representation for model-free posterior-sampling-style exploration. Standard critic ensembles do not naturally represent joint uncertainty over return functions across policy space, whereas functional-critic ensembles represent policy-conditioned return functions directly. This motivates a particle-based, model-free analogue of posterior-sampling-style exploration without explicit model learning. Finally, we instantiate these ideas in a deep functional actor-critic algorithm that, despite omitting several standard off-policy AC heuristics, achieves strong performance across the full Dog and Humanoid task series of the DeepMind Control Suite.
Chongyi Zheng, Zhiyuan Zhou, Qiyang Li, Benjamin Eysenbach, Sergey Levine
Learning accurate value functions is central to reinforcement learning (RL), yet remains challenging because supervision is limited and indirect: ground-truth values are unavailable, and each transition in the data only provides an immediate reward that can be used to construct bootstrapped targets as supervision. This challenge is particularly pronounced when data is limited or reward signals are sparse, as is common in real-world applications such as robotics. We argue that the key to accurate value learning in this limited data regime is extracting richer learning signals from the available data. In this paper, we introduce Value Forcing, a new approach that uses flow matching to estimate return distributions and enables accurate value learning from limited data. Our key insight is that we can extract richer learning signals by enforcing Bellman consistency at every noise level. Empirically, Value Forcing improves the accuracy of estimating the return distribution, and prevents the distribution from collapsing to a single value, as occurs with prior methods. We instantiate Value Forcing with two practical value-learning algorithms: VF, an actor-critic method, and IVF, an implicit value-learning method. We evaluate both algorithms across $26$ long-horizon tasks in the offline RL setting and find that they outperform prior value-learning methods, with particularly large gains when data is limited. Our project website is available at https://chongyi-zheng.github.io/value-forcing.
A Pretrained Model for Bayesian Filtering in Heterogeneous Dynamical Systems
Ke Fan, Wen Wu, Haitao Su, Yilin Wang, Yuan Zhou, Chao Zhang
AI 速览：提出首个面向贝叶斯滤波的预训练模型 FilterFM，用单一检查点跨异构动力系统估计隐状态，并报告零样本滤波与缩放行为。
Filtering has not yet had its foundation-model moment: state estimation is still dominated either by online Bayesian filtering algorithms that require known dy- namics as inputs, or by learning-based neural filters trained from scratch for a single system or parametric family. The core challenge is structural heterogene- ity: dynamical systems differ in their governing dynamics, physical scales, and noise levels, precluding a shared representation space for a reusable filter. To bridge this gap, we introduce FilterFM, the first pretrained model for Bayesian filtering that estimates latent states across heterogeneous dynamical systems with a single checkpoint. FilterFM employs a hierarchical two-level observation se- quence encoder and a prior-to-posterior state-reconstruction module that together handle varying observation and state dimensions across heterogeneous systems, while modeling state uncertainty via quantile prediction. Trained on a mixture of heterogeneous filtering tasks, FilterFM exhibits superior in-domain performance, delivering significant gains in estimation accuracy, runtime efficiency, and noise robustness compared to existing baselines. Beyond in-domain performance, it marks the first model to successfully enable zero-shot filtering on unseen dynamics, bridging the generalization gap in state estimation. Furthermore, we uncover clear scaling behaviors in foundation-model-based Bayesian filtering, which combined with comprehensive component ablation studies, validate the structural optimality of our design and broaden the generalization scope for this research field.
Zhiyuan Zhou, Andy Peng, Charles Xu, Qiyang Li, Jost Tobias Springenberg, Kevin Frans, Sergey Levine
Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control. While they are known to scale stably in the supervised imitation learning setting, incorporating them into reinforcement learning (RL) pipelines for policy improvement has proven more difficult. It often requires specialized training objectives or backpropagating through denoising processes, which cause well-known issues with stability and affect scalability. In this paper we study the question of whether simple policy improvement schemes at test time alone, leaving stable supervised policy training intact, can be a competitive alternative which sidesteps these issues. To this end, we propose QGF (Q-Guided Flow), an RL algorithm that performs policy optimization entirely at test time. QGF works by pre-training both a reference flow policy (via a standard behavioral cloning objective) and a value function critic and, at test time, using the value gradient to guide the reference policy to generate higher-value actions without any additional policy learning. Empirically, QGF outperforms prior test-time RL methods on single-task and goal-conditioned offline RL benchmarks with high-dimensional action spaces, and is competitive with state-of-the-art training-time algorithms while being much cheaper to run. Moreover, it exhibits favorable scaling with model size by avoiding the instability of actor-critic training, offering a practical and effective alternative RL algorithm with expressive policies.