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Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning

Kejian Zhu, Zhuoran Jin, Dongqi Huang, Hongbang Yuan, Yupu Hao, Kang Liu, Jun Zhao

arXiv:2608.03571v2cs.CV

TL;DR

Simply scaling multimodal environment pools can fluctuate or degrade performance, raising the question of how to design effective distributions. The paper proposes AES for diversity and HDC for difficulty structure, achieving a 143.2% average relative gain across settings.

  • Problem

    Existing multimodal environment distributions can cause performance degradation and negative transfer, motivating evaluation and design along diversity and difficulty structure.

  • Method

    AES selects environments by maximizing learned meta-ability coverage, while HDC schedules harness weakening and state-scale progression hierarchically.

  • Results

    143.2% average relative gain: AES + HDC improves training across ID/OOD environment groups and two model scales over the base model.

  • Takeaways & Limitations

    Effective multimodal environment distributions should be designed around both diversity and difficulty structure, which can complement each other in training.

  • Takeaways & Limitations

    The environment pool mainly reuses existing multimodal environments, while larger-scale environment synthesis remains unstudied because of cost constraints.

Abstract

from arXiv · show

Recent works train agents by constructing large-scale multimodal environment pools. However, we find that simply increasing the number of multimodal environments does not always benefit. We further analyze the limitations in current multimodal environment distributions through a series of experiments. Based on these findings, we study how to build more effective training environment distributions from two dimensions: **diversity** and **difficulty structure**. For diversity, we propose **Ability-aware Environment Selection (AES)** to obtain diverse environment sets. For difficulty structure, we propose **Hierarchical Difficulty Curriculum (HDC)**, which organizes curriculum learning through two difficulty levels: harness weakening and state-scale progression. Experiments show that AES and HDC effectively improve multimodal agent training.

1 Introduction

The paper argues that effective multimodal-agent training depends on environment distributions, not merely environment scale, and should be designed around diversity and difficulty structure. It proposes AES for diverse environment selection and HDC for multimodal difficulty progression, with both improving training outcomes.

  • Motivation: Increasing training environment types can cause substantial performance fluctuation or degradation, so more multimodal environments are not necessarily better.The finding comes from preliminary experiments using a pool of 200 environments.
  • Design dimensions: Effective environment distributions should provide broad ability coverage with low redundancy and optimization conflict, while supporting meaningful, stable difficulty progression.The paper evaluates distributions along diversity and difficulty structure.
  • Diversity: For diversity, Ability-aware Environment Selection analyzes agent trajectories and learned abilities rather than surface-level task-description representations.AES targets high coverage, low redundancy, and low optimization conflict.
  • Difficulty structure: For difficulty structure, Hierarchical Difficulty Curriculum addresses visual state extraction and world modeling through harness weakening and state-scale progression.The two-level curriculum targets multimodal-specific bottlenecks rather than relying only on state scale.
  • Results: 143.2% average relative gain is achieved by AES and HDC across different settings, while AES uses fewer environments and HDC improves stability and performance.The AES comparison is against the full environment pool; HDC is compared with direct training and conventional scale-only curriculum learning.

2 Preliminaries

Preliminary experiments show that simply scaling multimodal environment types can fail because mixed training causes stronger negative transfer and polarized gradient interactions. Multimodal failures also arise from visual state extraction and world-modeling limitations, motivating environment distributions designed around diversity and difficulty structure.

  • Experimental setup: The study unifies 200 multimodal environments from prior work and conducts controlled experiments to analyze problems in current multimodal environment distributions.Detailed experimental settings are provided in Appendix B.
  • Environment scaling: Model performance does not monotonically improve as the number of environment types increases, even with a fixed total compute budget.Agents are trained on the 200-environment pool while gradually increasing the number of training environment types.
  • Mixed-environment training: Mixed training causes a 1.3% drop in the text-symbolic setting but a 10.7% drop in the multimodal setting relative to single-environment training.The text-symbolic version is more stable, whereas the multimodal version exhibits stronger negative transfer.
  • Gradient interactions: Compared with text-symbolic environments, multimodal environments exhibit more polarized gradient similarities, including several strongly negative update-direction relationships.Gradient cosine similarity is used to assess consistency between training gradients, with negative values indicating conflict.
  • Multimodal bottlenecks: Manual analysis of 200 Qwen3-VL-4B failure trajectories identifies visual state extraction and world modeling as the main multimodal bottlenecks.These findings motivate designing environment distributions around diversity and difficulty structure rather than merely increasing usable environments.

3 Diversity: Environmental Selection

This section argues that environment diversity should be measured by the abilities agents must learn rather than surface representations. It introduces Ability-aware Environment Selection (AES), which selects environments for broad core-ability coverage while reducing redundancy and conflict, reaching full coverage with 30 environments.

  • Surface task descriptions and environment code do not necessarily reflect the abilities agents need to learn, motivating ability-based diversity measurement.
  • Ability-aware Environment Selection: AES derives ability requirements from 40 trajectories per environment collected from Qwen3-VL-4B and Gemini-3-Flash, capturing complementary successes and failures.GPT-5 annotates the trajectories to identify atomic abilities.
  • Ability-aware Environment Selection: Atomic abilities are merged into meta-abilities and represented as environment profiles containing ability frequencies and transition frequencies after filtering low-frequency, unstable abilities.
  • Ability-aware Environment Selection: AES scores candidates by new weighted ability coverage while accounting for profile similarity and gradient-based conflict, prioritizing broader coverage with less redundancy and interference.Core abilities receive higher coverage weights, and negative gradient cosine similarity indicates potential optimization interference.
  • Ability-aware Environment Selection: 30 environments selected by AES steadily increase core meta-ability coverage until reaching full coverage, indicating broad coverage with a small subset of the original pool.

4 Difficulty: Curriculum Learning

Multimodal agents struggle with raw visual inputs because they insufficiently extract visual states and model environmental rules, producing sparse rewards and unstable training. Hierarchical Difficulty Curriculum (HDC) addresses these bottlenecks through harness weakening and state-scale progression.

  • Motivation: Insufficient visual-state extraction and environmental-rule modeling make raw-input training difficult, causing sparse reinforcement-learning rewards and unstable training.Textual harnesses are introduced to help agents overcome these bottlenecks.
  • Harnesses: Four textual harnesses provide symbolic observations, key states, visual-content hints, or explicit task rules to support state extraction and rule understanding.The harnesses are textual observations, text states, text hints, and rule descriptions.
  • Hierarchical Difficulty Curriculum: HDC organizes training along two difficulty axes: harness weakening and state-scale progression.Harness weakening removes auxiliary scaffolds, while state-scale difficulty captures the complexity of environment instances such as grid size.
  • Harness Curriculum: Harness difficulty has five levels, H0–H4, progressing from full harnesses to basic raw visual observations and task descriptions.Earlier harness levels may still be sampled to avoid abrupt distribution shifts and mitigate forgetting.
  • Hierarchical Update: HDC advances state-scale within each harness level, then advances the harness level and resets state-scale after reaching the target scale and satisfying the harness threshold.Each environment independently maintains a harness frontier and state-scale sampling window, allowing environments to progress at different difficulty levels.

5 Experiments

Experiments on Qwen3-VL-4B/8B-Instruct evaluate AES and HDC against Random-K and All Envs. Results show that environment diversity and difficulty structure jointly improve multimodal agent training.

  • Experimental setup: Main experiments use Qwen3-VL-4B/8B-Instruct, with Random-K sampling 30 environments from the original 200-environment pool as a control baseline.Models are also evaluated on general multimodal benchmarks spanning different categories.
  • Diversity-aware environment selection (AES): AES achieves a 95.6% relative gain over the base model, compared with 43.4% for All Envs across ID/OOD groups and model scales.Training on AES-selected environments outperforms both Random-K and All Envs, indicating that larger environment pools can contain redundant or conflicting environments.
  • Difficulty-aware curriculum learning (HDC): Combining AES with HDC achieves a 143.2% average relative gain over the base model across ID/OOD groups and two model scales.HDC further improves performance across different environment sets, and diversity and difficulty complement each other.
  • AES ablation: AES ablations show that removing redundancy control lowers the OOD relative gain from 40.3% to 25.3%, while removing conflict control lowers it to 2.8%.The full AES achieves the largest relative gain over the base model on OOD environments.
  • HDC ablation: HDC ablations yield average relative gains of 11.5% for scale-only, 18.1% for harness-only, and 27.7% when both axes are combined.Harness weakening and state-scale progression are complementary and jointly improve multimodal environment training.

6 Related Works

The paper situates its work within multimodal environment and agent learning, emphasizing general multimodal environments and environment quality verification. Related verification studies examine executability and correctness of state transitions.

  • Research context: Multimodal environmental and agent learning have recently become cutting-edge research areas.The paper identifies these areas as the broader research context.
  • General multimodal environments: The paper mainly focuses on general multimodal environments within multimodal environment research.The cited works include Meng et al. (2026) and Wang et al. (2026b).
  • Environment quality verification: Another research line studies environment quality verification, including whether environments are executable and whether state transitions are correct.The passage attributes these verification concerns to Jain et al. (2025), Pan et al. (2025), and Hu et al.

7 Conclusion

Simply scaling multimodal environments is not always effective for agent training because environment-distribution effectiveness depends on diversity and difficulty structure. The paper proposes AES and HDC, which consistently improve agent training.

  • Simply scaling multimodal environments is not always effective for agent training.
  • Environment-distribution effectiveness depends on both diversity and difficulty structure.
  • AES constructs diverse environment sets, while HDC provides a multimodal-agent-specific hierarchical difficulty curriculum.
  • AES and HDC consistently improve agent training.

Limitations

The study is limited by its reliance on existing multimodal environment works and by constrained computational resources that restrict method comparisons to a unified compute budget.

  • Limitations: The environment pool mainly relies on existing multimodal environment works because of cost constraints, leaving large-scale environment synthesis for future exploration.The authors identify large-scale environment synthesis as an important direction for future work.
  • Limitations: Limited computational resources lead the authors to compare different methods primarily under a unified compute budget.This limitation concerns the computational setting used for method comparisons.

Ethics and Artifact Use Statement … B.1 General Settings

The paper documents responsible artifact use and situates its approach within multimodal environment and agent-learning research. It also specifies experimental training and evaluation settings for measuring performance and generalization.

  • Ethics and Artifact Use Statement: The work limits trained-model evaluation to controlled and verifiable multimodal environments because deployment without safeguards could produce incorrect actions or unintended effects.The stated potential risk is that environment-based training may improve decision making in interactive tasks.
  • Ethics and Artifact Use Statement: Its environment pool is built mainly from existing multimodal environment works for research, with cited assets used according to their licenses and terms.The environments are virtual and do not involve collecting personal data from real users.
  • Ethics and Artifact Use Statement: LLMs support atomic ability segmentation, environment profile construction, grammar polishing, and presentation improvements, while authors manually inspect outputs and verify experimental results remain unchanged.The authors also verify that writing assistance does not alter the original meaning.
  • A More Related Works; A.1 Multimodal Environments: The appendix reviews related work on environments and agent learning, including the shift from static multimodal datasets toward dynamic interactive environments and scalable Web, GUI, and game settings.The paper states that closely related works are discussed in the main thread in Section 6.
  • A.2 Sample-Level Environment Quality: Sample-level environment quality is commonly assessed through executability, reliable reward signals, sandbox execution, unit tests, state-transition correctness, and task solvability.These checks may use golden trajectories or expert tool sequences.
  • A.3 Environment Diversity: Distribution-level diversity is often controlled by removing semantically similar tasks using embedding similarity, deduplication, topic dispersion, or game-seed clustering.The passage identifies diversity as a key aspect of distribution-level quality.
  • A.4 Difficulty Grading and Curriculum Learning: Existing curricula grade difficulty using structural scale, model or expert calibration, learning signals, failed trajectories, or coarse-to-fine estimation, but multimodal difficulty also reflects modality-specific ability bottlenecks.Environment scale alone is therefore insufficient to characterize multimodal-environment difficulty.
  • A.5 Unsupervised Environment Design; B Experiment Details; B.1 General Settings: The experimental section first introduces settings shared across experiments, while general training uses Qwen3-VL-4B-Instruct, GRPO, rollout group size 16, and 7,680 total training samples under equal compute budgets.Qwen3-VL-8B-Instruct is additionally used to test effectiveness with a larger model; evaluation measures performance and generalization in ID and OOD settings, including 30 held-out environments from a 200-environment pool.

B.2 Settings for Preliminary Experiments … D.2 Harness Level

The paper fixes evaluation and compute settings for environment-scaling experiments, then details AES and HDC as structured approaches to environment selection and curriculum design. AES models environment abilities, while HDC progressively weakens harness support and increases state-scale difficulty.

  • B.2 Settings for Preliminary Experiments: Environment-scaling experiments use Qwen3-VL-4B with fixed compute, nested environment sets, and a shared 20-environment in-distribution evaluation subset.Larger environment sets contain smaller sets as subsets, ensuring comparable training-scale evaluations.
  • B.3 Settings for Main Experiments: Main experiments use the same compute budget across methods, with All Envs training on 170 environments after excluding 30 held-out environments.All Envs is used to assess whether naively scaling the environment pool is effective; larger-scale testing additionally includes Qwen3-VL-8B-Instruct.
  • B.3 Settings for Main Experiments: Main evaluation defines ID as AES-selected environments for most methods and random training environments for Random-K, while also testing general multimodal benchmarks.These benchmarks examine whether interactive-environment training harms general perception and reasoning ability.
  • C Details of Ability-aware Environment Selection (AES): AES constructs environment profiles by segmenting trajectories into reusable atomic abilities and aggregating them into interpretable environment-level meta abilities.Qwen3-VL-4B and Gemini-3-Flash generate trajectories, while GPT-5 performs atomic ability segmentation.
  • C.2 Construction of Environment Meta Ability Profiles: The profile-construction process merges similarly worded abilities, removes abilities below 10% frequency as noise, and classifies remaining abilities as core or soft.Core abilities are essential for solving tasks, whereas soft abilities are auxiliary, such as answer formatting.
  • C.3 Settings of the AES Method: AES scores candidate environments by maximizing core-ability coverage while reducing redundancy and conflict, assigning each term weight λ = 1.Redundancy uses maximum profile similarity with already selected environments as a penalty.
  • D Details of Hierarchical Difficulty Curriculum (HDC): HDC maintains per-environment curriculum states for harness and state-scale frontiers, samples both levels, and generates concrete training instances before reinforcement-learning updates.Its nested curriculum advances state scale within a harness level, then weakens the harness and restarts state-scale growth.
  • D.2 Harness Level: Harness curriculum progresses from H0, exposing textual observations, text state, hints, and complete rules, toward H4, which retains only basic visual observations and task descriptions.Auxiliary information is gradually removed as the harness level increases.

D.3 State-Scale Implementation Details · E More Experimental Results

The inner curriculum increases state-scale difficulty using environment-specific parameters, while additional experiments compare selection strategies and examine multimodal scaling under expanded training budgets.

  • D.3 State-Scale Implementation Details: The inner curriculum progresses through state-scale difficulty levels such as graph size, grid size, obstacle density, and entity count.Different environments support different state-scale parameters, and Table 8 summarizes adjustable factors for 30 selected environments.
  • D.3 State-Scale Implementation Details: DoorKey, Tents-QA, Arc1D, and LavaGap expose controllable map, grid, tree, sequence-length, example-count, or size parameters.Examples include DoorKey size=5/6/8/16, Tents-QA grid_size=[8,8] with num_trees=12, and Arc1D min_size=10, max_size=30, num_train=3.
  • D.3 State-Scale Implementation Details: Additional environments control difficulty through graph, board, grid, room, and blank-cell scales, including Othello, Frozen Lake, MultiRoom, and Mini Sudoku.Examples include Othello board_size=4/6/8/10, Frozen Lake size=4 with num_holes=5, and Mini Sudoku min_empty=8, max_empty=12.
  • D.3 State-Scale Implementation Details: Other environments vary tree-group size, point count, perturbation moves, repair distance, board graph size, or solution depth.Examples include Knight Swap min_nodes=9, max_nodes=10, min_steps=8, max_steps=20 and Matchstick Equation break_moves=2 with enforce_min_distance=True.
  • D.3 State-Scale Implementation Details: The scale-controllability inventory also includes graph density, binary-tree size, chess-piece count, array length, and predefined Rush Hour difficulty.Examples include max_n=14 with edge_density=0.6, Tree To Traversal min_nodes=12 and max_nodes=16, and Rush Hour difficulty=easy/medium/hard.
  • E More Experimental Results: Representation-based environment selection is evaluated against methods using embeddings of textual environment descriptions or key environment code.Table 9 distinguishes Description Embedding from Code Embedding according to the information embedded for selection.
  • E More Experimental Results: Expanded-budget scaling experiments fix the number of training samples per environment at 256.Figure 7 studies multimodal environment scaling under this expanded training budget.

E.1 Expanded-Budget Experiments … F Prompt for Atomic Ability Annotation

Across expanded-budget, transfer, selection, curriculum, and annotation analyses, the paper shows that effective environment distributions depend on more than environment count or surface similarity. AES and HDC improve training by selecting behaviorally diverse environments, structuring difficulty, and targeting specific failure modes.

  • E.1 Expanded-Budget Experiments: 66.3% ID success with 40 environments fell to 42.1% with 160, while OOD success peaked at 36.4% with 60 and fell to 23.6% with 160 under expanded budgets.Each environment received 256 training samples, showing that scaling degradation is not explained solely by insufficient per-environment training samples.
  • E.2 Performance Transfer across Modalities: Complete text-only and multimodal performance-transfer matrices are provided to supplement the summarized results reported for cross-environment transfer.The matrices appear in Tables 11 and 12, with rows representing training environments and columns representing evaluation environments.
  • E.3 Cross-Architecture Evaluation: OOD-ST improved from 13.4 to 18.3 and OOD-MT from 8.4 to 11.8 when InternVL3-8B used an AES subset selected with Qwen3-VL-4B.Under HDC, AES additionally exceeded Random-K by 3.1 points on OOD-ST and 4.6 points on OOD-MT without rerunning selection or annotation.
  • E.4 More Results on AES: AES-selected environments are well dispersed across the 200-environment profile space, while mixed human-defined categories indicate that source categories do not always match behavioral similarity.This supports evaluating diversity through agent behavior and training dynamics rather than relying only on human-defined categories.
  • E.6 Comparison with Curriculum-Learning Baselines.: HDC achieved the best performance across all evaluation settings and exceeded the strongest competing curriculum baseline by 5.9 points on multi-turn environments.All methods used the same AES-selected environments and matched total training budget, while harness weakening provided benefits beyond conventional curriculum learning.
  • E.7 Training Curves of HDC: HDC adaptively improves performance at each harness difficulty level, then resets to a harder harness setting and resumes state-scale progression after reaching the target scale.The Frozen Lake training curve visualizes this bi-level curriculum process.
  • E.8 Error Correction Analysis after HDC.: HDC substantially reduces visual state extraction and world modeling errors, the two major bottlenecks targeted by its curriculum design.The appendix also describes human annotation procedures and a prompt for converting rollout trajectories into atomic behavioral steps and abilities used to construct environment-level ability profiles and graphs.
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