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arxiv: 2605.20246 · v2 · pith:J25YCLGLnew · submitted 2026-05-18 · 💻 cs.LG · cs.AI

GROW: Aligning GRPO with State-Action Modeling for Open-World VLM Agents

Xiongbin Wu , Zhihao Luo , Shanzhe Lei , Lechao Zhang , Xuhong Wang , Jie Yang , Zhonglong Zheng , Yuanjie Zheng
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Xin Tan Wei Liu
This is my paper

Pith reviewed 2026-05-22 09:02 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords GRPOVLM agentsopen-world tasksMinecraftreinforcement learningtrajectory decompositionstate-action modelingmulti-turn RL
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The pith

GROW adapts GRPO for multi-turn VLM agents by decomposing trajectories into state-action samples for advantage computation.

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

The paper seeks to establish that standard GRPO cannot be directly used for multi-turn vision-language model agents in open-world settings because it treats each full trajectory as one training sample, producing excessively long contexts and noisy signals. GROW solves this by splitting collected trajectories into individual state-action samples and computing advantages across those samples instead of across whole trajectories. A surrogate analysis argues that this change still keeps the essential relative policy optimization behavior of GRPO when certain simplifying conditions hold. If the approach works, it would let researchers move beyond supervised fine-tuning with expert demonstrations and train VLM agents on hundreds of complex tasks using reinforcement learning signals that remain stable even when tasks require many perception-action turns.

Core claim

GROW decomposes collected trajectories into state-action samples and computes advantages between these samples rather than treating a full trajectory as a single entity. A surrogate analysis indicates that even though the grouped samples are conditioned on different local states rather than an identical prompt context, the objective can preserve the core relative policy optimization signal of GRPO under simplifying assumptions. Experiments on more than 800 Minecraft tasks show that this framework achieves state-of-the-art performance for open-world VLM agents.

What carries the argument

Decomposition of full trajectories into state-action samples followed by inter-sample advantage computation, which aligns the GRPO objective with state-action modeling for multi-turn agents.

Load-bearing premise

That samples drawn from different local states can still carry the same relative policy optimization signal as samples sharing an identical prompt context, provided the paper's simplifying assumptions remain valid.

What would settle it

An experiment that applies GROW to a long-horizon task where the local states differ substantially from any shared prompt context and measures whether the learned policy diverges from what standard GRPO would produce or loses its reported performance gains.

Figures

Figures reproduced from arXiv: 2605.20246 by Jie Yang, Lechao Zhang, Shanzhe Lei, Wei Liu, Xin Tan, Xiongbin Wu, Xuhong Wang, Yuanjie Zheng, Zhihao Luo, Zhonglong Zheng.

Figure 1
Figure 1. Figure 1: Context length in the trajectory in￾creases with the number of interaction steps be￾tween the VLM agent and the environment, often exceeding the maximum token length as interac￾tions accumulate. To address this issue, we propose GRPO for open￾world VLM agents (GROW), a RL framework that adapting GRPO to open-world tasks where trajec￾tories are often too long and agents make decisions often based on short-h… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of GROW, a RL framework for open-world VLM agents. Standard GRPO [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Performance comparison between the PPO baseline and GROW, our proposed RL frame [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Success rates on diagnostic tasks. GROW improves stable target fixation, active target reacquisition, and distractor-robust GUI operation. Kill Witch Out of Sight This task demonstrates active target reacquisition. Since the witch starts outside the field of view, the agent must search, recover visual contact, and continue pursuit as the target moves away. GROW achieves 50.0% success, whereas both baseline… view at source ↗
Figure 5
Figure 5. Figure 5: Step count histograms over ten rollouts for eight RL tasks. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Behavioral analysis of the agent engaging a Guardian in combat. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Case study of the agent synthesizing a Stone Shovel. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
read the original abstract

Recently, vision-language model (VLM) agents have shown promising progress in open-world tasks, where successful task completion often requires multiple turns of visual perception and action execution. However, existing methods still rely primarily on Supervised Fine-Tuning (SFT) with expert demonstrations, while the advanced reinforcement learning (RL) algorithm, specifically Group Relative Policy Optimization (GRPO), has not been effectively employed for multi-turn RL in these tasks because standard GRPO requires full trajectories as training samples which leads to excessively long context and noise. To address this issue, we propose GROW, a RL framework for open-world VLM agents that decomposes collected trajectories into state-action samples, and computes advantages between these samples rather than treating a full trajectory as a single entity. We further provide a surrogate analysis indicating that, even though the grouped samples are conditioned on different local states rather than an identical prompt context, the objective can preserve the core relative policy optimization signal of GRPO under simplifying assumptions. Experiments on more than 800 Minecraft tasks show that our method achieves state-of-the-art (SOTA) performance, demonstrating the effectiveness of our proposed RL framework for open-world VLM 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 / 2 minor

Summary. The manuscript presents GROW, a reinforcement learning framework for vision-language model (VLM) agents in open-world tasks such as Minecraft. It modifies Group Relative Policy Optimization (GRPO) by decomposing full trajectories into state-action samples and computing advantages across these samples rather than treating entire trajectories as single entities. This is intended to avoid excessively long contexts and noise. A surrogate analysis argues that the modified objective preserves the core relative policy optimization signal of GRPO under simplifying assumptions even when samples are conditioned on different local states. Experiments report state-of-the-art performance on more than 800 Minecraft tasks.

Significance. If the surrogate analysis is valid and the performance gains are robust, the work offers a practical route to applying relative policy optimization methods to multi-turn VLM agents, potentially improving sample efficiency and stability in long-horizon open-world settings by reducing reliance on full-trajectory training.

major comments (2)
  1. [Surrogate Analysis] Surrogate Analysis section: The claim that the objective preserves the core GRPO relative policy optimization signal rests on simplifying assumptions (identical prompt context replaced by different local states plus bounded variance in advantage estimates). In open-world Minecraft, where visual states differ sharply across steps, it is unclear whether the advantage estimator maintains the original relative ranking property; the manuscript does not provide a derivation showing invariance or empirical checks that the signal is recovered outside the assumed regime. This is load-bearing for the theoretical justification of the central method.
  2. [Experiments] Experiments section: The SOTA claim on more than 800 tasks is presented without quantitative details on baseline methods, exact success-rate metrics, number of independent runs, or statistical significance tests. Without these, it is difficult to assess whether the reported gains are attributable to the state-action decomposition or to other factors such as task selection or implementation details.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'surrogate analysis' is used without a one-sentence qualifier on the nature of the assumptions; adding this would improve readability for readers unfamiliar with the GRPO derivation.
  2. [Preliminaries] Notation: Define the state-action decomposition operator and the cross-sample advantage estimator explicitly in the preliminaries to ensure consistent interpretation across the method and analysis sections.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments highlight important aspects of the surrogate analysis and experimental reporting that we address point by point below. We have prepared revisions to strengthen the theoretical justification and provide the requested quantitative details.

read point-by-point responses

  1. Referee: [Surrogate Analysis] Surrogate Analysis section: The claim that the objective preserves the core GRPO relative policy optimization signal rests on simplifying assumptions (identical prompt context replaced by different local states plus bounded variance in advantage estimates). In open-world Minecraft, where visual states differ sharply across steps, it is unclear whether the advantage estimator maintains the original relative ranking property; the manuscript does not provide a derivation showing invariance or empirical checks that the signal is recovered outside the assumed regime. This is load-bearing for the theoretical justification of the central method.

    Authors: We agree that the surrogate analysis relies on simplifying assumptions and that additional support is needed to establish robustness outside this regime. In the original manuscript we show that, under the stated assumptions of bounded advantage variance and local-state conditioning, the relative ordering of advantages is preserved because the group-wise normalization remains monotonic with respect to the original GRPO signal. To address the referee’s concern, we will expand the appendix with a more explicit step-by-step derivation of the invariance property and add empirical checks that compare advantage rankings obtained from full-trajectory GRPO versus our state-action decomposition on a held-out subset of Minecraft tasks. These additions will clarify the conditions under which the core relative policy optimization signal is retained. revision: yes

  2. Referee: [Experiments] Experiments section: The SOTA claim on more than 800 tasks is presented without quantitative details on baseline methods, exact success-rate metrics, number of independent runs, or statistical significance tests. Without these, it is difficult to assess whether the reported gains are attributable to the state-action decomposition or to other factors such as task selection or implementation details.

    Authors: The referee correctly notes that the current experimental section lacks the quantitative details necessary for rigorous evaluation. In the revised manuscript we will add comprehensive tables that report per-task success rates for GROW and all baselines, the exact number of independent runs (five random seeds), and the results of statistical significance tests (paired t-tests with p-values). We will also include an ablation isolating the contribution of the state-action decomposition from other implementation choices. These changes will make the source of the reported gains transparent. revision: yes

Circularity Check

0 steps flagged

No significant circularity; surrogate analysis is an independent derivation under stated assumptions

full rationale

The paper's core contribution is a decomposition of trajectories into state-action pairs for advantage computation, followed by a surrogate analysis that derives preservation of the GRPO relative signal under explicit simplifying assumptions (different local states replacing identical prompt context). This analysis does not reduce the objective to its inputs by construction, nor does it rely on self-citation chains, fitted parameters renamed as predictions, or smuggled ansatzes. The derivation remains self-contained because the surrogate step is a separate mathematical argument whose validity can be checked against the stated assumptions without looping back to the empirical results or prior author work as load-bearing justification. No equations or sections exhibit the specific reductions required for circularity flags.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the surrogate analysis under simplifying assumptions and on the empirical results from Minecraft tasks; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Even though grouped samples are conditioned on different local states rather than an identical prompt context, the objective preserves the core relative policy optimization signal of GRPO under simplifying assumptions.
    Stated directly in the abstract as the justification for the decomposed training objective.

pith-pipeline@v0.9.0 · 5767 in / 1282 out tokens · 47397 ms · 2026-05-22T09:02:55.276742+00:00 · methodology

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