MARS Policy: Multimodality Only When It Matters

Bofan Lyu; Bohan Hou; Gen Li; Jianfei Yang; Jiaqi Bai; Jindou Jia; Jingliang Li; Tuo An; Xiangyu Chen; Yuxuan Hu

arxiv: 2605.29766 · v1 · pith:PES7PQX5new · submitted 2026-05-28 · 💻 cs.RO

MARS Policy: Multimodality Only When It Matters

Jindou Jia , Tuo An , Yuxuan Hu , Gen Li , Jingliang Li , Bohan Hou , Xiangyu Chen , Jiaqi Bai

show 2 more authors

Bofan Lyu Jianfei Yang

This is my paper

Pith reviewed 2026-06-29 06:39 UTC · model grok-4.3

classification 💻 cs.RO

keywords imitation learningrobotic manipulationmultimodal policiesadaptive samplinggenerative policiesdeterministic policiesinference efficiencybehavioral diversity

0 comments

The pith

MARS policy applies multimodal stochastic sampling only during robotic task phases that need behavioral diversity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that robotic imitation learning does not require constant multimodality because not every phase of a manipulation task needs diverse behaviors. Generative policies achieve diversity through ongoing stochastic noise and denoising but pay for it with complex training and slow inference. MARS instead detects single-modal phases and switches to deterministic prediction there, injecting noise only when it matters. This hybrid is meant to deliver the expressivity of generative methods together with the speed of deterministic ones. Tests on eight simulated and four real tasks are presented as evidence that the switch works without losing performance.

Core claim

The Modality-Adaptive Robot Sampling policy adaptively invokes tailored stochasticity only when it is truly beneficial while reverting to efficient deterministic learning during single-modal phases, thereby bridging multimodal capability with training and inference efficiency; real-world tests show a 16.67 percent success-rate gain and 83.20 percent latency reduction, and the method even improves training efficiency over pure deterministic policies on near-deterministic tasks by better capturing nuanced action diversity.

What carries the argument

The Modality-Adaptive Robot Sampling (MARS) policy, which selectively activates multimodal generation only at task phases identified as requiring behavioral diversity and otherwise uses deterministic prediction.

If this is right

Yields a 16.67 percent success-rate improvement over baselines in the four real-world tasks.
Delivers an 83.20 percent reduction in inference latency in the same real-world tests.
Surpasses pure deterministic policies in training efficiency even on tasks that are mostly single-modal.
Maintains robust multimodal expressivity across the eight simulated environments while using less compute.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The phase-detection logic could be reused in other sequential control settings where exploration is needed only at decision bottlenecks.
If the trigger for stochasticity can be learned without hand-crafted heuristics, the approach would scale to longer-horizon tasks with fewer manual interventions.
Hardware deployments on edge robots would become more practical because the deterministic segments avoid the repeated denoising cost of generative models.

Load-bearing premise

Not all phases of a robotic task inherently require behavioral diversity, and an adaptive mechanism can correctly identify when to invoke stochasticity versus determinism without adding overhead or errors.

What would settle it

A controlled comparison on the four real-world tasks in which the adaptive policy produces no measurable success-rate gain or latency reduction relative to a standard generative baseline would falsify the central claim.

read the original abstract

Imitation learning has become a cornerstone for solving complex robotic manipulation tasks. In particular, multimodality, which enables robots to capture diverse yet valid behavioral patterns, has driven the rapid emergence of generative policies as a dominant paradigm in robot learning. However, achieving such multimodality typically relies on stochastic noise initialization and iterative denoising procedures, resulting in substantial training complexity and low inference efficiency. Meanwhile, not all phases of a robotic task inherently require behavioral diversity. Motivated by this insight, we propose the Modality-Adaptive Robot Sampling (MARS) policy, which adaptively invokes tailored stochasticity only when it is truly beneficial, while reverting to an efficient deterministic learning during single-modal phases. In other words, the proper amount of noise is injected only at the proper time. By selectively activating multimodal generation, MARS policy bridges the gap between the multimodal capability of generative policies and the superior training and inference efficiency of deterministic models. Empirical studies across 8 simulated and 4 real-world tasks demonstrate that MARS exhibits robust multimodal expressivity and high efficiency, with a 16.67% success rate improvement and an 83.20% inference latency reduction in real-world tests. Counterintuitively, MARS also outpaces deterministic policies in training efficiency on near-deterministic tasks by more effectively modeling nuanced action diversity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MARS claims efficiency gains by switching to multimodal generation only in phases that need it, but the switching logic itself gets little direct validation.

read the letter

The paper's main contribution is a policy that runs deterministic most of the time and flips to stochastic multimodal sampling only when the current phase of a manipulation task actually benefits from behavioral diversity. They test this on 8 simulated tasks plus 4 real-world ones and report a 16.67% success-rate lift and 83.2% lower inference latency on the hardware runs. It also trains faster than a pure deterministic baseline even on near-deterministic tasks.

The adaptive switch is the piece that is new relative to standard generative or deterministic imitation policies. The empirical numbers are the part that actually matters for practitioners, because real robot deployment cares about both reliability and speed.

The soft spot is exactly the one the stress-test flags: there is no separate evidence shown that the phase detector is accurate or cheap. If it misclassifies a non-trivial fraction of steps, either success drops when noise is wrongly withheld or latency rises when it is wrongly added. The end-to-end gains are reported, but without ablations or metrics on the detector itself it is hard to know how much of the improvement is real versus an artifact of the particular tasks or baselines chosen. The abstract gives no error bars or exclusion rules either.

This is for people who already work on imitation learning for manipulation and want a practical efficiency tweak. A reader who needs a new theoretical angle or a fully dissected method will find less here.

I would send it to peer review. The real-world experiments give it enough weight to justify referee time, even if the switching validation needs to be tightened.

Referee Report

2 major / 1 minor

Summary. The paper proposes the Modality-Adaptive Robot Sampling (MARS) policy for imitation learning in robotic manipulation tasks. It argues that behavioral diversity is not needed in all task phases and introduces an adaptive mechanism to invoke stochastic multimodal generation only when beneficial, reverting to deterministic learning otherwise. This is claimed to combine the expressivity of generative policies with the efficiency of deterministic models. Experiments across 8 simulated and 4 real-world tasks are reported to show a 16.67% success rate improvement and 83.20% inference latency reduction in real-world tests, plus improved training efficiency on near-deterministic tasks.

Significance. If the adaptive detection mechanism can be shown to operate with high accuracy and negligible overhead, the result would provide a practical route to more efficient multimodal policies in robotics without sacrificing performance. The selective use of stochasticity addresses a real inefficiency in current generative approaches and could influence deployment of imitation-learned controllers on resource-constrained hardware.

major comments (2)

[Abstract / Empirical studies] The central empirical claims (16.67% success gain and 83.20% latency reduction) rest on the correctness of the phase-detection heuristic that decides when to enable stochasticity. No quantitative evaluation of this heuristic's accuracy, misclassification rate across phase transitions, or added computational cost appears in the reported experiments, leaving open the possibility that errors in the detector offset or negate the stated gains.
[Method description] The manuscript states that MARS 'adaptively invokes tailored stochasticity only when it is truly beneficial' but supplies no explicit description, pseudocode, or ablation of the detection rule itself (e.g., how single-modal vs. multi-modal phases are identified from observations or action distributions). Without this, the load-bearing adaptive component cannot be assessed for correctness or generality.

minor comments (1)

[Abstract] The abstract reports aggregate performance numbers without baseline descriptions, error bars, or data-exclusion criteria; these details should be added to the experimental section for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments correctly identify that the adaptive detection mechanism is central to the contribution and requires more explicit documentation and validation. We will revise the manuscript to address both points.

read point-by-point responses

Referee: [Abstract / Empirical studies] The central empirical claims (16.67% success gain and 83.20% latency reduction) rest on the correctness of the phase-detection heuristic that decides when to enable stochasticity. No quantitative evaluation of this heuristic's accuracy, misclassification rate across phase transitions, or added computational cost appears in the reported experiments, leaving open the possibility that errors in the detector offset or negate the stated gains.

Authors: We agree that quantitative validation of the heuristic is required to fully support the reported gains. In the revision we will add an analysis (new table and/or appendix) reporting detection accuracy, misclassification rates at phase boundaries, and measured computational overhead of the detector on the same task suites. This will allow readers to assess whether detector errors could offset the observed improvements. revision: yes
Referee: [Method description] The manuscript states that MARS 'adaptively invokes tailored stochasticity only when it is truly beneficial' but supplies no explicit description, pseudocode, or ablation of the detection rule itself (e.g., how single-modal vs. multi-modal phases are identified from observations or action distributions). Without this, the load-bearing adaptive component cannot be assessed for correctness or generality.

Authors: We acknowledge that the current manuscript does not provide a sufficiently explicit description or pseudocode of the phase-detection rule. The revised version will expand the Methods section with (i) a precise algorithmic statement of how single- versus multi-modal phases are identified from observations and action distributions, (ii) pseudocode, and (iii) an ablation study isolating the effect of the detection rule. These additions will make the adaptive component reproducible and allow assessment of its generality. revision: yes

Circularity Check

0 steps flagged

No circularity in provided derivation chain

full rationale

The abstract and available text contain no equations, parameter fits, self-citations, or derivations that reduce any claim to its inputs by construction. Claims rest on empirical results across tasks rather than tautological redefinitions or fitted inputs renamed as predictions. No load-bearing steps match the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, parameters, or modeling choices; ledger entries cannot be populated.

pith-pipeline@v0.9.1-grok · 5793 in / 1005 out tokens · 22134 ms · 2026-06-29T06:39:06.666004+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

11 extracted references · 8 canonical work pages · 6 internal anchors

[1]

FLASH: Efficient Visuomotor Policy via Sparse Sampling

Jiaqi Bai, Jindou Jia, Yuxuan Hu, Gen Li, Xiangyu Chen, Tuo An, Kuangji Zuo, and Jianfei Yang. Flash: Efficient visuomotor policy via sparse sampling.arXiv preprint arXiv:2605.15492,

work page internal anchor Pith review Pith/arXiv arXiv
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Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al.π0: A vision-language-action flow model for general robot control.arXiv preprint arXiv:2410.24164,

work page internal anchor Pith review Pith/arXiv arXiv
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OMP: One-step Meanflow Policy with Directional Alignment

Han Fang, Yize Huang, Yuheng Zhao, Paul Weng, Xiao Li, and Yutong Ban. Omp: One-step meanflow policy with directional alignment.arXiv preprint arXiv:2512.19347,

work page internal anchor Pith review Pith/arXiv arXiv
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Physical Intelligence, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Kevin Black, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Jared DiCarlo, et al.π∗ 0.6: A VLA that learns from experience.arXiv preprint arXiv:2511.14759, 2025a. Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, ...

work page internal anchor Pith review Pith/arXiv arXiv
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One-step flow policy: Self-distillation for fast visuomotor policies.arXiv preprint arXiv:2603.12480,

Shaolong Li, Lichao Sun, and Yongchao Chen. One-step flow policy: Self-distillation for fast visuomotor policies.arXiv preprint arXiv:2603.12480,

work page arXiv
[6]

ManiSkill: Generalizable manipulation skill benchmark with large-scale demonstrations.arXiv preprint arXiv:2107.14483,

Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, and Hao Su. ManiSkill: Generalizable manipulation skill benchmark with large-scale demonstrations.arXiv preprint arXiv:2107.14483,

work page arXiv
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Score-Based Generative Modeling through Stochastic Differential Equations

Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456,

work page internal anchor Pith review Pith/arXiv arXiv 2011
[8]

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Qinglun Zhang, Zhen Liu, Haoqiang Fan, Guanghui Liu, Bing Zeng, and Shuaicheng Liu. FlowPolicy: Enabling fast and robust 3D flow-based policy via consistency flow matching for robot manipulation. InProceedings of the AAAI Conference on Artificial Intelligence, pages 14754–14762, 2025a. Yichi Zhang, Yici Yan, Alexander Schwing, and Zhizhen Zhao. Towards hi...

work page internal anchor Pith review Pith/arXiv arXiv
[9]

and diffusion policy (Chi et al., 2025);Multimodal baselines, such as IBC (Florence et al.,

2025
[10]

and BET (Shafiullah et al., 2022);Deterministicpolicies, including A2A (Jia et al.,

2022
[11]

As shown in Fig

and its stochastic counterpart Noised-A2A, VITA (Gao et al., 2026), and ACT (Zhao et al., 2023). As shown in Fig. S2, while expert trajectories (a) demonstrate strategic multimodality, only stochastic models (b-g) can represent the underlying distribution. Notably, generative (b-d) achieves superior fidelity and cleaner trajectories compared to other meth...

2026

[1] [1]

FLASH: Efficient Visuomotor Policy via Sparse Sampling

Jiaqi Bai, Jindou Jia, Yuxuan Hu, Gen Li, Xiangyu Chen, Tuo An, Kuangji Zuo, and Jianfei Yang. Flash: Efficient visuomotor policy via sparse sampling.arXiv preprint arXiv:2605.15492,

work page internal anchor Pith review Pith/arXiv arXiv

[2] [2]

Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al.π0: A vision-language-action flow model for general robot control.arXiv preprint arXiv:2410.24164,

work page internal anchor Pith review Pith/arXiv arXiv

[3] [3]

OMP: One-step Meanflow Policy with Directional Alignment

Han Fang, Yize Huang, Yuheng Zhao, Paul Weng, Xiao Li, and Yutong Ban. Omp: One-step meanflow policy with directional alignment.arXiv preprint arXiv:2512.19347,

work page internal anchor Pith review Pith/arXiv arXiv

[4] [4]

Physical Intelligence, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Kevin Black, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Jared DiCarlo, et al.π∗ 0.6: A VLA that learns from experience.arXiv preprint arXiv:2511.14759, 2025a. Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, ...

work page internal anchor Pith review Pith/arXiv arXiv

[5] [5]

One-step flow policy: Self-distillation for fast visuomotor policies.arXiv preprint arXiv:2603.12480,

Shaolong Li, Lichao Sun, and Yongchao Chen. One-step flow policy: Self-distillation for fast visuomotor policies.arXiv preprint arXiv:2603.12480,

work page arXiv

[6] [6]

ManiSkill: Generalizable manipulation skill benchmark with large-scale demonstrations.arXiv preprint arXiv:2107.14483,

Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, and Hao Su. ManiSkill: Generalizable manipulation skill benchmark with large-scale demonstrations.arXiv preprint arXiv:2107.14483,

work page arXiv

[7] [7]

Score-Based Generative Modeling through Stochastic Differential Equations

Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456,

work page internal anchor Pith review Pith/arXiv arXiv 2011

[8] [8]

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Qinglun Zhang, Zhen Liu, Haoqiang Fan, Guanghui Liu, Bing Zeng, and Shuaicheng Liu. FlowPolicy: Enabling fast and robust 3D flow-based policy via consistency flow matching for robot manipulation. InProceedings of the AAAI Conference on Artificial Intelligence, pages 14754–14762, 2025a. Yichi Zhang, Yici Yan, Alexander Schwing, and Zhizhen Zhao. Towards hi...

work page internal anchor Pith review Pith/arXiv arXiv

[9] [9]

and diffusion policy (Chi et al., 2025);Multimodal baselines, such as IBC (Florence et al.,

2025

[10] [10]

and BET (Shafiullah et al., 2022);Deterministicpolicies, including A2A (Jia et al.,

2022

[11] [11]

As shown in Fig

and its stochastic counterpart Noised-A2A, VITA (Gao et al., 2026), and ACT (Zhao et al., 2023). As shown in Fig. S2, while expert trajectories (a) demonstrate strategic multimodality, only stochastic models (b-g) can represent the underlying distribution. Notably, generative (b-d) achieves superior fidelity and cleaner trajectories compared to other meth...

2026