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arxiv: 2606.26006 · v1 · pith:IJHQ73SXnew · submitted 2026-06-24 · 💻 cs.RO · cs.AI

FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation

Shuyi Zhang , Yunfan Lou , Hongyang Cheng , Yichen Guo , Chuyao Fu , Yaoxu Lyu , Xiaojie Zhang , Haoran Li
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Pengwei Wang Zhongyuan Wang Shanghang Zhang
This is my paper

Pith reviewed 2026-06-25 19:26 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords Vision-Language-ActionReinforcement LearningFine-TuningValue FunctionRoboticsPolicy OptimizationSample Efficiency
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The pith

FORCE stabilizes reinforcement learning fine-tuning of vision-language-action models by calibrating the Q-function before online updates to avoid early unlearning.

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

Vision-language-action models are limited by imitation on suboptimal data, yet reinforcement learning can exceed that ceiling if its sample inefficiency is fixed. FORCE introduces a three-stage process that begins with on-policy rollouts to calibrate the Q-function and shrink its distributional shift. The calibrated Q-function then filters both the policy's action proposals and expert data so that only high-value actions drive the policy update. Experiments across simulation and real-world tasks report a 79 percent absolute rise in success rates, 10 percent gains over earlier RL methods, 32.5 percent faster training, and stable performance without any human intervention.

Core claim

FORCE is a three-stage framework that first runs a Value-Calibrated Warm-Up with on-policy rollouts to reduce Q-function distributional shift, then uses the resulting Q-function as a filter on both policy-generated and expert actions during the online stage, thereby enabling stable RL fine-tuning of VLA models that delivers a 79 percent absolute success-rate improvement, outperforms prior RL methods by 10 percent, shortens training by 32.5 percent, and removes the need for human intervention.

What carries the argument

Value-Calibrated Warm-Up phase that uses on-policy rollouts to align the Q-function distribution so it can later filter high-value actions for policy updates.

Load-bearing premise

On-policy rollouts during the warm-up phase can sufficiently correct the Q-function distributional shift to stop catastrophic unlearning when RL fine-tuning of VLA models begins.

What would settle it

A controlled run that applies the online RL stage immediately after standard pre-training and still records a sharp early drop in success rate would show that the warm-up calibration step is not sufficient to stabilize the process.

Figures

Figures reproduced from arXiv: 2606.26006 by Chuyao Fu, Haoran Li, Hongyang Cheng, Pengwei Wang, Shanghang Zhang, Shuyi Zhang, Xiaojie Zhang, Yaoxu Lyu, Yichen Guo, Yunfan Lou, Zhongyuan Wang.

Figure 1
Figure 1. Figure 1: Overview of the FORCE framework. Our method employs a three-stage reinforcement fine-tuning pipeline that progressively calibrates value estimation and stabilizes policy improvement: (1) offline Cal-QL pretraining to obtain a conservative and well-grounded critic, (2) mixed-rollout value pre-calibration to bridge offline and online distributions and mitigate O2O drift, and (3) online fine-tuning with balan… view at source ↗
Figure 2
Figure 2. Figure 2: VGPD module in the online phase. VGPD serves as a regularized policy improvement mechanism. We maintain an ex￾pert buffer and a policy buffer. For states sampled from the policy buffer, we compute a dynamic value baseline Vref(s) (approxi￾mated by Qmean). The policy is updated via filtered importance sampling, distilling only from actions that show positive advantage over this baseline. A critical challeng… view at source ↗
Figure 3
Figure 3. Figure 3: Real-world Experiment Tasks. We conducted real￾world experiments using a single-arm Franka robot equipped with two RealSense cameras that supplied complementary visual feed￾back: a wrist-view and a side-view. 4.1. Overview of Experiments We designed our empirical evaluation to verify the theo￾retical claims of the FORCE framework. Specifically, we investigate whether addressing distributional shift and en￾… view at source ↗
Figure 4
Figure 4. Figure 4: Learning Curves. We present the performance of our proposed FORCE and baselines in ManiSkill tasks. The evaluation spans three random seeds. FORCE consistently demonstrates faster convergence and higher final performance, validating the benefits of distributional warm-up and value-guided updates [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Ablation Study. Training curves across four tasks comparing full FORCE against variants without warm-up or VGPD. The results highlight the necessity of mitigating initial distribution shift and filtering policy updates. 2. Real-World Reliability: Does the VGPD mechanism provide a more stable and sample-efficient learning sig￾nal compared to standard gradient-based RL updates? 3. Intervention-Free Convergen… view at source ↗
Figure 6
Figure 6. Figure 6: Adaptive Nature of VGPD. We visualize the proportion of ”active” learning targets derived from the policy itself (Self￾Distillation) versus the offline buffer over time. The mechanism naturally transitions from cloning to exploration. 4.4. Adaptive Distillation Dynamics To understand how VGPD regulates the learning process, we analyze the source of the distillation targets throughout training ( [PITH_FULL… view at source ↗
Figure 7
Figure 7. Figure 7: Simulation experiments on six ManiSkill manipulation tasks. PlaceSphere: place a small sphere onto a goal pad; StackCube: stack one cube on another; PickCube: pick and lift a target cube; PushCube: push a cube into a goal region; PullCube: pull a cube into a goal region; PullCubeTool: use an L-shaped tool to drag a cube that starts outside the robot’s reachable workspace into the goal region. Clean Whitebo… view at source ↗
Figure 8
Figure 8. Figure 8: Camera view of successful trajectories on real-world experiment tasks. B.2. Task Descriptions Pick Cup: Pick up a cup and place it onto a plate. Open Drawer: Open the top drawer of a cabinet. Insert USB: Insert a USB into a port with precise alignment. Pick Corn: Pick up a corn from a cluttered environment and place it on a plate. Stack Cube: Stack a red cube on top of a blue cube. Clean Whiteboard: Erase … view at source ↗
read the original abstract

Vision-Language-Action (VLA) models are often constrained by the imitation ceiling imposed by sub-optimal data. While Reinforcement Learning (RL) fine-tuning can surpass this limit, it is notoriously sample inefficient. This challenge arises from two core issues: (1) catastrophic initial unlearning due to an unstable Q-function and (2) inefficient policy updates caused by low-quality exploration data, often forcing a reliance on costly human interventions. We introduce FORCE, a 3-stage framework that stabilizes fine-tuning by tackling both issues. FORCE first incorporates a Value-Calibrated Warm-Up phase, utilizing on-policy rollouts to mitigate the distributional shift of the Q-function. Subsequently, during the online stage, this calibrated Q-function acts as a filter for both the policy's own action proposals and expert data, ensuring only high-value actions are used for the policy update. We evaluate FORCE on various simulation and real-world tasks, and the result shows that FORCE achieves a 79% absolute improvement in success rates and outperform prior RL methods by 10%, while accelerating training by 32.5%. Critically, it mitigates the common success rate drop and achieves this robust performance without human intervention, marking a significant step towards deploying capable and autonomous robotic agents.

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.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces FORCE, a three-stage framework for RL fine-tuning of Vision-Language-Action (VLA) models. Stage 1 performs Value-Calibrated Warm-Up via on-policy rollouts to mitigate Q-function distributional shift and prevent catastrophic initial unlearning. Stage 2 uses the calibrated Q as a filter on both policy proposals and expert data to retain only high-value actions. Stage 3 applies self-distillation. The authors claim this yields a 79% absolute success-rate improvement, 10% gains over prior RL methods, 32.5% faster training, elimination of the typical success-rate drop, and fully autonomous operation without human intervention on simulation and real-world tasks.

Significance. If the reported gains are substantiated and the Value-Calibrated Warm-Up is shown to be the causal factor via isolated verification, the work would constitute a meaningful advance in sample-efficient RL for VLAs by reducing reliance on human interventions and addressing the imitation ceiling.

major comments (2)
  1. [Abstract and Method section] Abstract and Method section: The central claim that the on-policy Value-Calibrated Warm-Up mitigates Q-function distributional shift enough to avoid catastrophic unlearning is load-bearing for the 79% absolute improvement and 32.5% acceleration. No supporting quantitative evidence is supplied (Q-value variance plots, Wasserstein distance between pre- and post-warm-up Q distributions, or an ablation that removes only the calibration step while retaining filtering and self-distillation). Without such isolation it remains possible that gains arise from the Q-filter, self-distillation, or task selection rather than the claimed mechanism.
  2. [Experiments section] Experiments section: Performance numbers (79% absolute improvement, 10% outperformance, 32.5% acceleration) are stated without any description of baselines, number of runs, statistical significance tests, task specifications, or reward definitions. This absence prevents verification that the data support the mechanism claims.
minor comments (1)
  1. [Abstract] The abstract refers to 'various simulation and real-world tasks' without naming them or providing any equations that define the value calibration or filtering procedure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing the need for isolated evidence on the Value-Calibrated Warm-Up mechanism and fuller experimental details. We address each major comment below and will revise the manuscript to strengthen these aspects.

read point-by-point responses

  1. Referee: [Abstract and Method section] The central claim that the on-policy Value-Calibrated Warm-Up mitigates Q-function distributional shift enough to avoid catastrophic unlearning is load-bearing for the 79% absolute improvement and 32.5% acceleration. No supporting quantitative evidence is supplied (Q-value variance plots, Wasserstein distance between pre- and post-warm-up Q distributions, or an ablation that removes only the calibration step while retaining filtering and self-distillation). Without such isolation it remains possible that gains arise from the Q-filter, self-distillation, or task selection rather than the claimed mechanism.

    Authors: We agree that the manuscript currently lacks quantitative isolation of the warm-up's causal contribution. In the revision we will add: (1) an ablation that disables only the Value-Calibrated Warm-Up while retaining the Q-filter and self-distillation stages, (2) Q-value variance plots before and after warm-up, and (3) distributional shift metrics (Wasserstein distance) between pre- and post-warm-up Q distributions. These additions will directly test whether the warm-up step is responsible for avoiding the initial success-rate drop. revision: yes

  2. Referee: [Experiments section] Performance numbers (79% absolute improvement, 10% outperformance, 32.5% acceleration) are stated without any description of baselines, number of runs, statistical significance tests, task specifications, or reward definitions. This absence prevents verification that the data support the mechanism claims.

    Authors: We acknowledge the current Experiments section is insufficiently detailed for independent verification. The revised version will expand this section to include: explicit descriptions and citations for all baselines, the number of independent runs with random seeds, statistical significance tests (e.g., paired t-tests across seeds), complete task specifications (environments, success criteria, episode lengths), and the precise reward function definitions used in both simulation and real-world settings. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on experimental outcomes independent of self-referential definitions or fits.

full rationale

The manuscript describes a 3-stage pipeline (Value-Calibrated Warm-Up via on-policy rollouts, Q-filtering of actions, self-distillation) and reports measured success-rate gains, training acceleration, and absence of human intervention. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. Performance numbers are presented as external validation results on simulation and real-world tasks rather than quantities forced by the method's own definitions or prior author work. The load-bearing assumption about Q-function calibration is an empirical hypothesis, not a definitional reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identifiable from the abstract alone; the method description does not introduce new mathematical objects or fitted constants.

pith-pipeline@v0.9.1-grok · 5793 in / 1294 out tokens · 33893 ms · 2026-06-25T19:26:30.057132+00:00 · methodology

discussion (0)

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