5arXiv cs.AI (Artificial Intelligence)·
DLAM: Distributional latent action model with temporal constraints for robot policy learning
DLAM introduces a distributional latent-action model that represents robot action transitions as diagonal Gaussians, enabling structured extraction of action priors from action-free video data. The approach uses normalized composition and reversal over equal-gap triplets to constrain both mean and variance, addressing error propagation in recursive composition that affects prior deterministic methods. A flow-matching policy jointly generates mean transition sequences and robot actions, and the method outperforms latent-action baselines on MetaWorld MT50, LIBERO, and real-world manipulation tasks under a controlled π₀ transfer protocol.
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AHA-WAM: Asynchronous world-action modeling with temporal decoupling for robot manipulation
AHA-WAM introduces a dual Diffusion Transformer architecture that decouples world prediction (low-frequency) from action execution (high-frequency) in robot manipulation policies, addressing the inefficiency of existing world-action models that force both branches to operate at the same temporal resolution. The system uses a rolling key-value memory video DiT as a long-horizon scene planner and a fast action DiT that queries layerwise latent context via joint attention, with Observation-Guided Video-Context Routing enabling asynchronous execution. On RoboTwin benchmarks, AHA-WAM achieves 92.80% average success and 78.3% on real-world tasks at 24.17 Hz, a 4.59x speedup over Fast-WAM, without robot-data pretraining.
Latent Memory Palace: Variational inference framework enables adaptive reasoning for continuous control policies
Researchers introduce Latent Memory Palace (LMP), a method that formulates reasoning for continuous control policies as variational inference over an autoregressive latent distribution, analogous to a memory palace. The approach derives a latent-space reinforcement learning technique to optimize the variational lower bound, yielding a policy (LMP-π) with adaptive test-time compute allocation and a variable-length action tokenizer (LMP-tok) that improves downstream autoregressive policies. The work addresses the gap between language model chain-of-thought reasoning and continuous control, where language-space reasoning lacks spatial granularity. Results are demonstrated in both simulation and real-world domains.
KGRL: Neuro-symbolic reinforcement learning for parametrized action spaces using Datalog knowledge bases
A new arXiv preprint introduces KGRL (Knowledge- and Gradient-Guided Reinforcement Learning), a neuro-symbolic algorithm for Parametrized Action Markov Decision Processes (PAMDPs) where each decision involves both a symbolic action and continuous numerical parameters. KGRL uses a Datalog knowledge base to prune infeasible actions and constrain parameter spaces, then applies gradient-based refinement to estimate optimal parameters during training and deployment. The approach improves sample efficiency and episodic return over state-of-the-art PAMDP baselines, and provides local procedural explanations for its decisions.
Geometric Action Model (GAM) repurposes geometric foundation models for 3D-aware robot manipulation
Researchers propose the Geometric Action Model (GAM), a language-conditioned robot manipulation policy that splits a pretrained geometric foundation model (GFM) to serve simultaneously as an observation encoder, causal future predictor, and action decoder. Unlike existing vision-language-action models that operate on 2D image frames, GAM explicitly incorporates 3D geometric priors for contact-rich manipulation. The approach claims improvements in accuracy, robustness, speed, and model size over foundation-model-scale baselines across simulation and real-robot benchmarks.
Mask-Aware Policy Gradients improve RL training for Masked Diffusion Language Models
A new arXiv preprint introduces a two-stage action MDP formalization for applying reinforcement learning to Masked Diffusion Language Models (MDLMs), decomposing the policy gradient into a token prediction term and a masking order term. Prior approaches ignored the position-unmasking decision, leading to intractable log-likelihood estimates; the proposed method optimizes both terms jointly. The approach achieves 87.1% on GSM8K and 53.4% on MBPP, claiming state-of-the-art results for MDLM-based reasoning and coding.
Adaptive Data Scheduling (ADS) improves LLM reinforcement learning post-training by 5.2% over GRPO
Researchers propose Adaptive Data Scheduling (ADS), a dual-level framework that replaces uniform sampling in RL post-training with adaptive distribution over semantic clusters and policy-boundary sample selection. Evaluated across three LLMs and seven reasoning benchmarks, ADS improves average accuracy by 5.2% over GRPO and generalizes across RL objectives. The method addresses a structural limitation in standard RL post-training pipelines by accounting for semantic data structure and evolving policy capability during training.
RECALL: Active continual learning for Vision-Language-Action models via uncertainty-guided recovery data collection
Researchers propose RECALL, an active continual learning paradigm for Vision-Language-Action (VLA) robot models that uses uncertainty-guided data collection to target states where the policy struggles, rather than passively collecting demonstrations after failures. The paper demonstrates improved fine-tuning efficiency over passive imitation learning but identifies catastrophic forgetting as a key challenge when incorporating recovery data. The authors evaluate continual learning mitigations including replay-based data mixing and elastic weight consolidation, characterizing tradeoffs between plasticity and retention in large autoregressive robot policies.
DynaFLIP: Dynamics-Aware Multimodal Pre-Training for Robot Manipulation Perception
DynaFLIP is a pre-training framework that injects motion understanding into visual encoders for robot manipulation by constructing image-language-3D flow triplets from human and robot videos. The method encourages tri-modal alignment via simplex-volume minimization in a shared hyperspherical space, combined with cosine regularization and contrastive objectives. The resulting dynamics-aware visual backbone consistently outperforms baselines across diverse downstream policies including VLAs, with gains up to +22.5% in out-of-distribution scenarios. The work argues that robot generalization requires encoding how the world changes under action, not just static scene content.