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arxiv: 2607.00483 · v2 · pith:PL44FVL6new · submitted 2026-07-01 · 💻 cs.RO

VLM-AR3L: Vision-Language Models for Absolute and Relative Rewards in Reinforcement Learning

Kuan-Chen Chen , Winston Chen , Wei-Fang Sun , Min-Chun Hu This is my paper

Pith reviewed 2026-07-03 20:41 UTC · model grok-4.3

classification 💻 cs.RO
keywords reinforcement learningvision-language modelsreward learningabsolute rewardsrelative rewardsembodied AIMinecraft
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The pith

VLM-AR3L trains reinforcement learning agents with both absolute state scores and relative progress judgments generated by vision-language models.

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

The paper presents VLM-AR3L as a way to address the difficulty of designing rewards in reinforcement learning, especially for abstract goals in open environments. It has vision-language models interpret agent observations against a natural language task description, then produces two kinds of signals: absolute rewards that score single states and relative rewards that compare pairs of observations to detect forward or backward movement. These two signals are combined to guide learning. The resulting method is tested on control tasks, manipulation, and complex embodied settings including Minecraft, where it shows stronger results than earlier VLM reward approaches.

Core claim

VLM-AR3L learns an absolute reward model that outputs scalar evaluations for individual states and a relative reward model that compares consecutive observations to determine progress or regression toward the task goal, both derived from preference labels supplied by a vision-language model. Their integration supplies the stability of direct state evaluation together with the robustness of comparative supervision, and this combined reward function produces higher-performing policies than prior VLM-based reward learning methods on benchmarks that include classic control, manipulation, and long-horizon open-world tasks.

What carries the argument

The dual reward architecture that fuses an absolute scalar state evaluator with a relative comparative progress detector, both trained on VLM-generated preference labels for a given natural language goal.

If this is right

  • The combined absolute and relative signals produce more stable learning trajectories than either signal alone.
  • The approach scales to long-horizon decision making where visual complexity makes hand-designed rewards impractical.
  • Performance gains appear across classic control, robotic manipulation, and open-world embodied environments.
  • Relative comparisons add robustness when absolute state values are noisy or hard to calibrate.

Where Pith is reading between the lines

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

  • The same dual-signal structure could be tested with other multimodal models that accept both images and language.
  • If the language goal is vague or changes over time, an online update mechanism for the VLM labels might be needed.
  • In domains where visual observations are low-dimensional, the relative component may contribute less than the absolute one.
  • Extending the method to multi-agent settings would require the VLM to judge joint observations against shared goals.

Load-bearing premise

VLM-generated preference labels correctly indicate whether one observation shows progress or regression toward the stated task goal, without systematic biases that would distort the learned reward models.

What would settle it

Training a reinforcement learning policy with VLM-AR3L rewards on a held-out Minecraft task and observing no performance gain or a clear drop relative to a strong baseline reward method would indicate the central claim does not hold.

Figures

Figures reproduced from arXiv: 2607.00483 by Kuan-Chen Chen, Min-Chun Hu, Wei-Fang Sun, Winston Chen.

Figure 1
Figure 1. Figure 1: Comparison of absolute and relative reward formu￾lations. Left: Conceptual illustration in a simple Markov process. Absolute reward assigns scalar values to states, where higher scores indicate proximity to the goal. Relative reward supervises transi￾tions by assessing progress between state pairs. Right: Empirical reward trajectories evaluated along the same expert demonstration at different training step… view at source ↗
Figure 2
Figure 2. Figure 2: RingWorld environment and reward comparison. Left: Illustration of the RingWorld task, where the agent is required to move clockwise along a closed loop. Right: Learning curves for agents trained with absolute or relative reward, showing that ab￾solute reward fails under the cyclic state ordering, whereas relative reward enables successful learning. • Relative reward modeling: We train a Siamese net￾work w… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the VLM-AR3L framework. During reward model training (blue path), observation pairs are sampled from the agent’s replay buffer and evaluated by a VLM to determine which observation better aligns with the task goal. The resulting preference labels are used to supervise both absolute and relative reward models. During policy learning (purple path), the trained absolute model evaluates individual … view at source ↗
Figure 4
Figure 4. Figure 4: Environments and tasks used in our experiments, spanning [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Learning curves of all evaluated methods across tasks. The x-axis represents training timesteps. The y-axis denotes episode rewards measured by the ground-truth dense reward. Results are averaged over 3 random seeds with 5 evaluation episodes per checkpoint. Shaded regions indicate standard error. rates of all evaluated methods, and [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation studies of the relative reward architecture across [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ablation studies of absolute and relative rewards. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Ablation studies of temporal offset k [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Ablation studies of weighting coefficient alpha. E.2 Ablation VLM See [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Ablation results with different vision-language models. [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Ablation studies of absolute and relative rewards. [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
read the original abstract

Designing effective reward functions remains a major challenge in reinforcement learning (RL), particularly in open-ended environments where task goals are abstract and difficult to quantify. In this work, we present VLM-AR3L, a framework that leverages Vision-Language Models (VLMs) to provide both absolute and relative rewards for RL. VLM-AR3L interprets an agent's visual observations in the context of a natural language task goal, and learns both absolute and relative rewards from VLM-generated preference labels. The absolute reward model predicts scalar evaluations for individual states, while the relative reward model compares consecutive observations to infer progress or regression toward the task goal. Their integration combines the stability of state-based evaluation with the robustness of comparative supervision. We evaluate VLM-AR3L across benchmarks spanning classic control, manipulation, and open-world embodied tasks, with a particular focus on Minecraft given its visual complexity and long-horizon decision-making requirements. Experimental results show that VLM-AR3L consistently outperforms prior VLM-based reward learning methods.

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 paper introduces VLM-AR3L, a framework that uses vision-language models to generate both absolute rewards (scalar state evaluations) and relative rewards (comparisons of consecutive observations for progress toward natural-language goals) to supervise RL agents. It integrates the two reward heads for stability and robustness, and reports consistent outperformance over prior VLM-based reward methods on classic control, manipulation, and especially Minecraft benchmarks.

Significance. If the empirical claims hold after proper validation, the work would be significant for reward learning in long-horizon, visually complex embodied settings. Combining absolute and relative VLM supervision is a reasonable architectural idea that could improve sample efficiency and stability over purely comparative or purely scalar approaches; the Minecraft focus is well-chosen given the domain's partial observability and horizon length.

major comments (2)
  1. [Abstract / Experiments] Abstract and Experiments section: the central claim that VLM-AR3L 'consistently outperforms prior VLM-based reward learning methods' is unsupported by any reported metrics, baselines, error bars, or statistical tests. Without these, the data-to-claim link cannot be evaluated and the outperformance assertion remains unevaluable.
  2. [Experiments] Experiments section (Minecraft results): the headline result requires that VLM-generated preference labels are free of systematic biases (e.g., over-weighting salient but irrelevant pixels or failing on partial observability across dozens of steps). No ablation, human validation of labels, or noise-injection study is described; if such biases exist, both reward heads are trained on corrupted signals and any reported gains could be artifacts rather than evidence for the absolute-relative integration.
minor comments (2)
  1. [Method] The manuscript would benefit from explicit equations defining the absolute reward head r_abs(s) and relative reward head r_rel(s_t, s_{t+1}) and how they are combined into the final reward signal.
  2. [Figures / Tables] Figure and table captions should include the exact number of seeds, evaluation episodes, and whether results are mean ± std.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We agree that the empirical claims require stronger quantitative support and validation of the VLM labels. Below we address each major comment and commit to revisions that will include the requested metrics, statistical tests, and label-quality analyses.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments section: the central claim that VLM-AR3L 'consistently outperforms prior VLM-based reward learning methods' is unsupported by any reported metrics, baselines, error bars, or statistical tests. Without these, the data-to-claim link cannot be evaluated and the outperformance assertion remains unevaluable.

    Authors: We acknowledge the need for more rigorous reporting. In the revised manuscript we will expand the Experiments section with tables reporting mean returns and standard deviations over at least 5 random seeds for all methods, explicit numerical comparisons against every baseline, error bars on all learning curves, and statistical significance tests (paired t-tests with p-values) to support the outperformance statements. The abstract will be updated to reference these quantitative results. revision: yes

  2. Referee: [Experiments] Experiments section (Minecraft results): the headline result requires that VLM-generated preference labels are free of systematic biases (e.g., over-weighting salient but irrelevant pixels or failing on partial observability across dozens of steps). No ablation, human validation of labels, or noise-injection study is described; if such biases exist, both reward heads are trained on corrupted signals and any reported gains could be artifacts rather than evidence for the absolute-relative integration.

    Authors: We agree this is an important concern. The revised version will add: (i) a human validation study on a random sample of VLM preference labels with inter-annotator agreement metrics, (ii) an ablation that injects controlled label noise and measures degradation in both reward heads, and (iii) a short discussion of how the joint absolute-relative objective provides robustness to label errors. These additions will directly address whether observed gains could be artifacts. revision: yes

Circularity Check

0 steps flagged

Empirical framework with no self-referential derivations or fitted predictions

full rationale

The manuscript describes an applied RL framework that trains separate absolute and relative reward heads from VLM-generated preference labels on visual observations. No equations, uniqueness theorems, or ansatzes are presented that reduce any claimed result to its own inputs by construction. Evaluation relies on external benchmarks (classic control, manipulation, Minecraft) rather than internal consistency checks. Self-citations, if present, are not load-bearing for the core method. This is a standard empirical contribution whose validity rests on experimental outcomes, not definitional closure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no free parameters, axioms, or invented entities can be identified from the provided text.

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Reference graph

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