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Training Strategies for Efficient Embodied Reasoning
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Training Strategies for Efficient Embodied Reasoning
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Robot chain-of-thought reasoning (CoT) -- wherein a model predicts helpful intermediate representations before choosing actions -- provides an effective method for improving the generalization and performance of robot policies, especially vision-language-action models (VLAs). While such approaches have been shown to improve performance and generalization, they suffer from core limitations, like needing specialized robot reasoning data and slow inference speeds. To design new robot reasoning approaches that address these issues, a more complete characterization of why reasoning helps policy performance is critical. We hypothesize several mechanisms by which robot reasoning improves policies -- (1) better representation learning, (2) improved learning curricularization, and (3) increased expressivity -- then devise simple variants of robot CoT reasoning to isolate and test each one. We find that learning to generate reasonings does lead to better VLA representations, while attending to the reasonings aids in actually leveraging these features for improved action prediction. Our results provide us with a better understanding of why CoT reasoning helps VLAs, which we use to introduce two simple and lightweight alternative recipes for robot reasoning. Our proposed approaches achieve significant performance gains over non-reasoning policies, state-of-the-art results on the LIBERO-90 benchmark, and a 3x inference speedup compared to standard robot reasoning.
Forward citations
Cited by 21 Pith papers
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TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches
Physical adversarial patches can steer CoT-reasoning VLAs into attacker-specified manipulation behaviors without changing the user’s instruction.
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Dense per-frame intermediate representations (traces, masks, grasp poses, subtasks) improve embodied VQA, VLA action generation, and world-model video prediction in the new 230k-episode RoboInter-Data suite.
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Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
Pre-training a VLA model on 100k hours of auto-labeled UMI trajectories, then post-training on robot data, yields SOTA simulated manipulation and data-efficient fine-tuning.
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APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts
A VLM planner that adaptively inserts latent visual thoughts of future states into its reasoning trace beats language-only and prior VLM planners on long-horizon kitchen tasks, especially under tight free space.
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A learned critic of observation grounding and stepwise coherence, used as dense RL reward, improves VLA planner faithfulness and OOD hazard responsiveness while preserving competitive trajectory accuracy.
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Training Vision-Language-Action Models with Dense Embodied Chain-of-Thought Supervision
ZR-0 trains a dual-stream VLA model on 60 million frames with dense ECoT annotations so that high-level reasoning transfers across single-arm, bimanual, and humanoid embodiments while skipping reasoning at inference.
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Training Vision-Language-Action Models with Dense Embodied Chain-of-Thought Supervision
ZR-0 is a dual-stream VLA model trained with dense ECoT supervision on 60M frames from 400K trajectories to enable cross-embodiment transfer in simulation and real-world settings.
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DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
DIRECT is a multimodal-context router that allocates test-time compute across chain-of-thought depth, model size, and memory history for VLM embodied planners, improving the success-cost Pareto frontier and matching s...
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Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation
ERVLA trains on a 978k-trajectory embodied CoT corpus using reasoning as supervision with dropout, then predicts actions without CoT at test time, reaching 86.9% on LIBERO-Plus and 53.2% on VLABench.
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Beyond Thinking: Imagining in 360$^\circ$ for Humanoid Visual Search
Imagining in 360° decouples visual search into a single-step probabilistic semantic layout predictor and an actor, removing the need for multi-turn CoT reasoning and trajectory annotations while improving efficiency i...
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PoseVLA: Universal Pose Pretraining for Generalizable Vision-Language-Action Policies
Pretraining a vision-language model to output discrete 3D pose tokens on large non-robotic data, before training a robot action head, improves downstream manipulation success and data efficiency.
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PoseVLA: Universal Pose Pretraining for Generalizable Vision-Language-Action Policies
Pose-VLA uses a decoupled two-stage pre-training with discrete pose tokens to extract universal 3D spatial priors from 3D datasets and robotic trajectories, achieving 79.5% success on RoboTwin 2.0 and 96.0% on LIBERO.
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Unified Embodied VLM Reasoning with Robotic Action via Autoregressive Discretized Pre-training
A 6K-question embodied-reasoning benchmark plus a flow-matching action tokenizer let one 3B vision-language model reason and manipulate better than continuous- or discrete-action VLA baselines.
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EVE: A Generator-Verifier System for Generative Policies
Zero-shot VLM verifiers, ensembled and fused via guided diffusion, improve frozen generative robot policies' success rates by 1-2 percentage points on simulated manipulation tasks.
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DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models
DeepThinkVLA shows CoT improves VLA models only under decoding and causal alignment, delivering 97% success on LIBERO and 21.7-point gains via hybrid attention and SFT-RL training.
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SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
SimpleVLA-RL applies tailored reinforcement learning to VLA models, reaching SoTA on LIBERO, outperforming π₀ on RoboTwin, and surpassing SFT in real-world tasks while reducing data needs and identifying a 'pushcut' p...
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Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
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GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions
GraphCoT-VLA couples structured chain-of-thought planning and a real-time 3D object-pose graph to improve robot manipulation under vague instructions.
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Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey
A survey that groups VLA efficiency techniques into four categories: model architecture, perception features, action generation, and training/inference strategies.
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