arxiv: 2604.28192 · v3 · submitted 2026-04-30 · 💻 cs.RO · cs.CV

Recognition: unknown

LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning

Hao Chen , Jiaming Liu , Zhonghao Yan , Nuowei Han , Renrui Zhang , Chenyang Gu , Jialin Gao , Ziyu Guo

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Siyuan Qian Yinxi Wang Peng Jia Chi-Wing Fu Shanghang Zhang Pheng-Ann Heng

Authors on Pith no claims yet

Pith reviewed 2026-05-08 02:55 UTC · model grok-4.3

classification 💻 cs.RO cs.CV

keywords robotic manipulationreinforcement learninglatent reasoningchain of thoughtvision language actionpolicy optimizationLIBERO benchmark

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The pith

LaST-R1 uses reinforcement learning to jointly optimize latent Chain-of-Thought reasoning and actions for robotic manipulation.

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

The paper presents LaST-R1 as a reinforcement learning post-training method that embeds adaptive latent reasoning before actions. It claims this produces stronger physical world modeling than static imitation learning alone. The approach reportedly reaches 99.9 percent average success on the LIBERO benchmark after minimal supervised warm-up and improves real-world performance by up to 22.5 percent across four complex tasks.

Core claim

By embedding latent Chain-of-Thought reasoning directly inside the RL optimization loop via the LAPO algorithm and adding an adaptive horizon mechanism, the policy learns to model physical dynamics more robustly, enabling near-perfect simulated success and measurable real-world gains over supervised fine-tuning baselines.

What carries the argument

Latent-to-Action Policy Optimization (LAPO) jointly optimizes the latent reasoning process and action generation while an adaptive latent CoT mechanism dynamically adjusts the reasoning horizon based on environment state.

If this is right

Robotic policies can reach high success rates with only one-shot supervised initialization followed by RL fine-tuning.
Adaptive reasoning length improves handling of diverse environment states without manual tuning per task.
The same latent-reasoning RL loop transfers from simulation to real single-arm and dual-arm settings.
Explicit embedding of reasoning steps inside the policy gradient update strengthens generalization over pure imitation methods.

Where Pith is reading between the lines

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

The method may reduce the data needed for new manipulation skills if latent reasoning transfers across related tasks.
Future extensions could test whether the same adaptive CoT approach improves performance in partially observable or long-horizon tasks beyond the four evaluated.

Load-bearing premise

Jointly optimizing latent reasoning and action generation through LAPO will reliably produce stable, non-overfit physical world models without extra post-training adjustments.

What would settle it

A controlled experiment showing that removing the latent reasoning component or fixing the CoT horizon length eliminates the reported gains on LIBERO or real-robot tasks.

Figures

Figures reproduced from arXiv: 2604.28192 by Chenyang Gu, Chi-Wing Fu, Hao Chen, Jialin Gao, Jiaming Liu, Nuowei Han, Peng Jia, Pheng-Ann Heng, Renrui Zhang, Shanghang Zhang, Siyuan Qian, Yinxi Wang, Zhonghao Yan, Ziyu Guo.

**Figure 1.** Figure 1: LaST-R1. (a) Unlike vanilla RL baselines that strictly optimize actions, (b) our approach utilizes LAPO to jointly optimize an adaptive latent CoT alongside physical execution. By bridging cognitive reasoning and control, LaST-R1 achieves (c) faster convergence speed, higher success rate in simulation, and (d) stronger generalization capabilities in real-world scenarios. capture continuous, high-frequency … view at source ↗

**Figure 2.** Figure 2: Overview. (a) LaST-R1 is a unified VLA model that takes visual observations and language instructions as input, where a vision foundation model provides physically grounded latent targets to guide latent CoT reasoning before action generation. (b) During LAPO RL post-training, the policy interacts with the environment in a closed loop manner, storing latent tokens, actions, and rewards in a rollout buffer … view at source ↗

**Figure 3.** Figure 3: Online RL learning curves on LIBERO. We compare our proposed LaST-R1 optimized via LAPO (red), against the standard Action-Only baseline optimized via PPO (blue). Baselines. We conduct a comprehensive comparison against both SFT-only and RL-trained models. All SFT baselines are trained on the full training dataset (50 trajectories per task). Among the RL-trained baselines, GRAPE [38] utilizes DPO [39], VLA… view at source ↗

**Figure 4.** Figure 4: Ablation studies. We evaluate (a) latent representation methods, (b) different fixed latent CoT lengths, and (c) adaptive CoT length with varying <latent_end> placements. boosts performance from 26.2% to 48.6%. During RL post-training, our full method (LaST-R1 + LAPO) further amplifies this advantage, achieving 99.8% SR across all task suites and significantly outperforming Action-Only + PPO (94.6%). These… view at source ↗

**Figure 5.** Figure 5: Generalization analysis on LIBERO. While the OOD performance of the Action-Only PPO baseline (blue) stagnates, our LaST-R1 with LAPO (red) demonstrates continuous improvement. hexagon block task, a precise single-arm manipulation, our proposed method boosts the success rate from 45% to 90%, highlighting its effectiveness in refining visually guided precision behaviors. For the dual-arm tasks, Open bag zipp… view at source ↗

**Figure 6.** Figure 6: Hybrid Attention Mask Design. Our model employs a custom attention mask to unify view at source ↗

**Figure 7.** Figure 7: Ablation studies on loss coefficients. Performance impact of varying (a) latent loss weight view at source ↗

**Figure 8.** Figure 8: Frequency distribution of adaptive latent reasoning lengths. Compared to the SFT warm-up view at source ↗

**Figure 9.** Figure 9: Comparison of average execution steps across LIBERO task suites. We report the average view at source ↗

**Figure 10.** Figure 10: Generalization analysis on LIBERO. For each task suite, models are warmed up with one view at source ↗

**Figure 11.** Figure 11: Real-world execution trajectories of the proposed policy. The sequences illustrate the view at source ↗

**Figure 12.** Figure 12: Policy robustness in visually diverse and cluttered environments. The model consis view at source ↗

**Figure 13.** Figure 13: Visualizations of Action-to-Vision Cross-Attention. We compare the attention maps across view at source ↗

read the original abstract

Robotic foundation models require reasoning over complex visual scenes to execute adaptive actions in dynamic environments. While recent studies on latent-reasoning Vision-Language-Action (VLA) models have demonstrated the capability to capture fine-grained physical dynamics, they remain predominantly confined to static imitation learning, severely limiting their adaptability and generalization. In this paper, we present LaST-R1, a novel reinforcement learning (RL) post-training framework designed to effectively harness "latent reasoning-before-acting" policies. Specifically, we propose Latent-to-Action Policy Optimization (LAPO), a core RL algorithm that jointly optimizes the latent reasoning process and the action generation. By explicitly embedding latent Chain-of-Thought (CoT) reasoning directly within the RL optimization loop, LAPO stimulates profound physical world modeling, which in turn drives robust execution in interactive environments. Furthermore, an adaptive latent CoT mechanism is introduced, allowing the policy to dynamically modulate its reasoning horizon based on diverse environment states. Experiments show that LaST-R1 achieves a near-perfect 99.9% average success rate on the LIBERO benchmark with only one-shot supervised warm-up, significantly improving convergence speed and performance over prior state-of-the-art (SOTA) methods. In real-world deployments, LaST-R1 yields up to a 22.5% average improvement over SOTA supervised fine-tuning approach across four complex tasks, including both single-arm and dual-arm settings. Finally, LaST-R1 demonstrates strong generalization across simulated and real-world environments.

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

LaST-R1 folds latent CoT reasoning into an RL loop via LAPO and reports near-perfect LIBERO scores plus real-world gains, but the experimental support for the adaptive mechanism stays thin.

read the letter

The main thing to take away is that this paper puts forward LAPO, an RL post-training algorithm that optimizes latent reasoning and action generation together in VLA models, plus an adaptive CoT horizon that changes with the state. The headline numbers are 99.9 percent average success on LIBERO after one-shot supervised warm-up and up to 22.5 percent real-world improvement over supervised fine-tuning on four tasks. That combination of latent reasoning inside the RL objective and dynamic horizon adjustment is the concrete novelty relative to prior imitation-only VLA work. The real-robot single-arm and dual-arm results give the claims a bit more weight than pure simulation papers usually carry. If the gains come from better physical modeling rather than tuning, the approach could help make these models more practical in changing environments. The positioning against static imitation learning is clear and the method description is concrete enough to replicate in principle. On the soft spots, the abstract and reported results lean heavily on the final success rates without showing ablations for the adaptive component, training stability across seeds, or detailed failure analysis. It is not obvious whether the fast convergence is robust or tied to specific benchmark properties and hyperparameter choices. The sim-to-real transfer section also lacks enough detail on how distribution shift was handled to judge how general the 22.5 percent lift really is. This paper is aimed at groups working on post-training for robotic foundation models and latent reasoning in VLAs. Readers who already run RL on these models will find the LAPO formulation worth testing even if they modify it. It deserves a serious referee because the empirical claims are large enough and the core idea is testable, though the authors should expect questions on component contributions and reproducibility. I would send it out for peer review.

Referee Report

3 major / 2 minor

Summary. The paper introduces LaST-R1, a reinforcement learning post-training framework for Vision-Language-Action (VLA) robotic models. It proposes Latent-to-Action Policy Optimization (LAPO) to jointly optimize embedded latent Chain-of-Thought reasoning and action generation within the RL loop, along with an adaptive mechanism to dynamically adjust the latent CoT horizon based on environment states. Central claims include achieving a 99.9% average success rate on the LIBERO benchmark after only one-shot supervised warm-up (outperforming prior SOTA in convergence and performance) and up to 22.5% average improvement over supervised fine-tuning baselines in real-world single-arm and dual-arm tasks, with strong sim-to-real generalization.

Significance. If the empirical results hold under rigorous validation, this work would advance the field by showing how RL can be used to elicit robust physical world modeling from latent reasoning in VLA policies, moving beyond static imitation learning. The explicit embedding of CoT into the optimization objective and the adaptive horizon are potentially impactful for improving adaptability in dynamic manipulation tasks.

major comments (3)

[§4 (Experiments)] §4 (Experiments), LIBERO results: the 99.9% average success rate is presented without reported standard deviations, number of evaluation seeds, or statistical significance tests against baselines; this is load-bearing for the claim of significant improvement in convergence speed and performance over prior SOTA.
[§3.2 (LAPO)] §3.2 (LAPO formulation): the joint optimization of latent reasoning and action generation is described at a high level but lacks any analysis of training stability, potential for latent space collapse, or sensitivity to the adaptive CoT horizon length; this directly underpins the central assumption that LAPO produces reliable physical modeling without instability or overfitting.
[§4.3 (Real-world)] §4.3 (Real-world deployments): the 22.5% average improvement across four tasks is stated without specifying per-task success criteria, number of trials, or variance; this is necessary to evaluate the sim-to-real transfer claim and rule out selection effects.

minor comments (2)

[Abstract] The abstract and §3 introduce LAPO and adaptive CoT but could expand the first-use definition of 'one-shot supervised warm-up' to clarify the exact number of demonstrations used.
[§4] Add a dedicated ablation subsection or table isolating the contribution of the adaptive horizon versus fixed CoT to strengthen attribution of gains.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will incorporate revisions to strengthen the experimental reporting and analysis.

read point-by-point responses

Referee: [§4 (Experiments)] §4 (Experiments), LIBERO results: the 99.9% average success rate is presented without reported standard deviations, number of evaluation seeds, or statistical significance tests against baselines; this is load-bearing for the claim of significant improvement in convergence speed and performance over prior SOTA.

Authors: We agree that standard deviations, the number of evaluation seeds, and statistical significance tests are necessary to rigorously support the performance claims. In the revised manuscript, we will report results from multiple evaluation seeds with standard deviations and include statistical tests comparing LaST-R1 to prior SOTA baselines. revision: yes
Referee: [§3.2 (LAPO)] §3.2 (LAPO formulation): the joint optimization of latent reasoning and action generation is described at a high level but lacks any analysis of training stability, potential for latent space collapse, or sensitivity to the adaptive CoT horizon length; this directly underpins the central assumption that LAPO produces reliable physical modeling without instability or overfitting.

Authors: We acknowledge the value of explicit analysis on these aspects. The revised manuscript will include additional experiments and discussion on training stability (via loss and reward curves), monitoring for latent space collapse, and ablations on CoT horizon sensitivity to demonstrate that LAPO yields stable and reliable physical modeling. revision: yes
Referee: [§4.3 (Real-world)] §4.3 (Real-world deployments): the 22.5% average improvement across four tasks is stated without specifying per-task success criteria, number of trials, or variance; this is necessary to evaluate the sim-to-real transfer claim and rule out selection effects.

Authors: We agree that per-task success criteria, trial counts, and variance measures are required for full transparency. The revision will expand §4.3 with a detailed table or description specifying success definitions, number of trials per task, and performance variance for each of the four real-world tasks. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on empirical benchmarks

full rationale

The paper introduces LaST-R1 and LAPO as an RL post-training framework that embeds latent CoT reasoning into the optimization loop, with an adaptive horizon mechanism. However, the provided text (abstract and description) contains no equations, derivations, or first-principles predictions. Central claims of 99.9% LIBERO success and 22.5% real-world gains are presented as experimental outcomes after one-shot warm-up, not as results forced by self-definition, fitted parameters renamed as predictions, or self-citation chains. No load-bearing step reduces to its own inputs by construction; the work is self-contained as standard empirical RL robotics research.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; all claims appear empirical.

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