Scalable Environments Drive Generalizable Agents
Jiayi Zhang, Fanqi Kong, Guibin Zhang, Maojia Song, Zhaoyang Yu, Jianhao Ruan, Jinyu Xiang, Bang Liu
Abstract
Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such generalization requires environment scaling: expanding the distribution of executable rule-sets that agents interact with, rather than only increasing trajectories or tasks within fixed benchmarks. Current scaling practices largely focus on collecting more experience or broader task sets under fixed interaction rules, leaving agents brittle when underlying interfaces, dynamics, observations, or feedback signals change. The core challenge is therefore a world-level distribution shift: agents need systematic exposure to environments with meaningfully different executable rule-sets. To clarify this challenge, we propose a unified taxonomy that separates trajectory scaling, task scaling, and environment scaling by their primary deliverables and by what changes in the executable rule-set. Building on this taxonomy, we synthesize construction paradigms for scalable environments, contrasting programmatic generators that prioritize controllability and verifiability with generative world models that offer broader coverage and open-endedness. We further outline how environment scaling can be coupled with stateful learning mechanisms, emphasizing learned update rules for cross-environment adaptation. We conclude by discussing alternative perspectives and argue that scalable environments provide the essential substrate for measurable and controllable progress toward robust general agents.
AI Impact Assessments
(1 model)Scientific Impact Assessment: "Scalable Environments Drive Generalizable Agents"
1. Core Contribution
This position paper introduces a conceptual taxonomy distinguishing three regimes of scaling for agent training: trajectory scaling (more interaction traces under fixed rules), task scaling (broader objectives under fixed rules), and environment scaling (expanding the distribution of executable rule-sets themselves). The central thesis is that cross-environment generalization—adapting to changes in action interfaces, dynamics, observations, and feedback signals—requires systematic exposure to diverse environments with meaningfully different executable rule-sets, not merely more data or tasks within a fixed world.
The paper formalizes an environment as an executable rule-set E = (S, A, T, O, Ω, Y) and introduces a behavioral distinguishability criterion d_beh(E, E'; H, Π) to operationalize when two environments are meaningfully different from an agent's perspective. It further contrasts two paradigms for constructing scalable environments: programmatic generators (controllable, verifiable) and generative world models (broader coverage, open-ended). The evaluation criteria framework (executability, signals, coverage, complexity, efficiency) provides a practical checklist for the community.
2. Methodological Rigor
As a position paper, this work is inherently conceptual rather than empirical. The formalization is clean and internally consistent—the POMDP-inspired notation, the separation of agent mental state modules, and the behavioral distinguishability metric are all well-defined. However, several concerns arise:
3. Potential Impact
The paper addresses a genuine and increasingly recognized gap. As LLM-based agents proliferate across web, code, and embodied domains, their brittleness to interface changes is a practical concern. The taxonomy could serve as a shared vocabulary for the community, helping researchers position their contributions more precisely and identify blind spots.
Practical implications include:
However, the impact is limited by the absence of empirical demonstrations. The paper does not show that environment scaling actually leads to measurably better cross-environment generalization compared to trajectory or task scaling alone. Without such evidence, the central thesis remains a plausible but unproven hypothesis.
4. Timeliness & Relevance
The paper is well-timed. The agent community is experiencing rapid growth, with numerous concurrent efforts in tool-use agents, web agents, and embodied agents. The observation that current benchmarks often test narrow generalization is widely shared but rarely formalized. The paper cites very recent work (2025-2026), indicating engagement with the cutting edge. The "Era of Experience" framing from Silver & Sutton (2025) and the emergence of text-to-world systems like Genie 3 make environment scaling a natural next step for discussion.
The timeliness is also reflected in the growing concern about benchmark saturation—agents achieving high scores on fixed benchmarks while failing in deployment. This paper articulates a structural explanation for this phenomenon (world-level distribution shift) and proposes a pathway forward.
5. Strengths & Limitations
Strengths:
Limitations:
Overall Assessment
This is a well-structured position paper that introduces a useful conceptual framework for a timely problem. Its primary value lies in organizing a fragmented landscape and providing shared vocabulary rather than in technical novelty. The lack of empirical evidence supporting the central thesis limits its scientific impact, though the framework itself may influence how future work is designed and reported.
Generated May 19, 2026
Comparison History (22)
Paper 1 addresses a fundamental and broadly impactful question about how to build generalizable agents through environment scaling, proposing a unifying taxonomy and research agenda that could shape the entire field of agent learning. Its scope spans reinforcement learning, foundation models, and open-ended learning. Paper 2, while practically valuable for LLM agent safety, addresses a narrower problem (trajectory-level hallucination detection) with a specific dataset/benchmark contribution. Paper 1's conceptual framework has greater potential to influence multiple research communities and drive long-term paradigm shifts in agent training methodology.
While Paper 1 offers a strong, concrete methodological advancement in human-AI collaboration, Paper 2 presents a foundational paradigm shift for general AI. By formalizing 'environment scaling' and proposing a unified taxonomy for agent generalization, Paper 2 addresses a critical bottleneck in reinforcement learning. Its conceptual contributions have the potential to broadly influence future research directions, benchmark design, and scaling laws across multiple subfields of AI, yielding a higher overall scientific impact.
Paper 2 likely has higher scientific impact because it presents a concrete, training-free, inference-time method with demonstrated state-of-the-art improvements on widely used benchmarks (CHAIR, POPE, MME), making it immediately actionable for real LVLM deployments. Its region-aware attention recalibration is a clear algorithmic contribution with broad applicability across vision-language generation systems and strong timeliness given current focus on hallucination and reliability. Paper 1 is a compelling position/taxonomy on “environment scaling,” but it is less directly validated experimentally, making near-term measurable impact and adoption more uncertain.
Paper 1 addresses a fundamental challenge in AI—building generalizable agents through environment scaling—which has broad implications across reinforcement learning, robotics, and foundation model research. Its unified taxonomy and synthesis of construction paradigms (programmatic generators vs. generative world models) provide a conceptual framework that could influence multiple research communities. Paper 2, while methodologically rigorous in formalizing trust calibration as preferential Bayesian optimization, addresses a narrower problem (human-AI trust in tool use) with more limited cross-field impact. Paper 1's timeliness, given the current surge in agent research, further amplifies its potential influence.
Paper 2 has higher likely impact because it delivers a concrete, reproducible benchmark substrate with released data, metrics, and large-scale empirical characterization (n=23,375), enabling immediate methodological comparisons and progress on delegation/orchestration—an increasingly timely real-world need for agentic systems. Its multi-axis metrics and counterfactual ceiling provide rigorous evaluation signals beyond end-task quality, with applicability across LLM routing, systems, and HCI. Paper 1 is a compelling, novel framing and taxonomy, but as a position paper it is less methodologically grounded and offers fewer directly actionable artifacts for the community.
Paper 1 introduces a concrete, novel safety architecture (evidence-carrying multimodal agents) with typed certificates and deterministic gating, directly addressing a timely, high-stakes failure mode (hallucination-to-action conversion) in deployed multimodal/tool-using systems. It includes substantial empirical evaluation (red-teaming at scale, measured bypass reductions, end-to-end unsafe-action rates) indicating methodological rigor and near-term applicability to security, HCI, and agent design. Paper 2 is a valuable conceptual position on environment scaling and taxonomy, but is less empirically grounded and more incremental relative to existing discussions on generalization via diverse environments.
Paper 2 likely has higher scientific impact because it frames a broad, timely research agenda for generalizable agents, introducing a clear taxonomy (trajectory/task/environment scaling) and motivating a shift in scaling methodology that could influence many subfields (RL, robotics, world models, evaluation/benchmarks). Its conceptual contribution is widely applicable and could reshape how generalization is measured and pursued. Paper 1 is more method-specific and rigorous with concrete gains, but its impact is narrower (multi-task unlearning in vision) and less cross-cutting.
Paper 1 addresses a fundamental challenge in AI—building generalizable agents through environment scaling—proposing a novel taxonomy and framework with broad implications across reinforcement learning, robotics, and foundation model research. Its position-paper format tackles a timely, high-level question relevant to the entire AI community. Paper 2, while methodologically sound, focuses on a narrow application (mastering Schnapsen with shallow RL), offering incremental contributions with limited generalizability beyond card game AI. Paper 1's breadth of impact, novelty in framing, and relevance to current scaling discussions give it substantially higher potential scientific impact.
Paper 1 presents a concrete, novel algorithm (on-policy hindsight self-distillation) that addresses a well-known bottleneck in search-augmented RL—step-level credit assignment for queries—without external teachers or annotations, making it practical and directly testable. It is likely to yield measurable performance gains and be adopted in real systems, with clear methodological contributions (training objective, conditioning scheme) and near-term relevance to LLM agents. Paper 2 is a compelling conceptual/taxonomy position piece with broad framing, but offers fewer immediately verifiable methods, so near-term scientific and practical impact is less certain.
Paper 2 has higher potential impact because it proposes a concrete, implementable method (WorldString) for learning actionable object state manifolds from real sensory inputs, aligning with timely needs in robotics, AR/VR, and digital twins. This offers clearer real-world applications and a more testable, methodologically grounded contribution than Paper 1, which is primarily a position/taxonomy paper. While environment scaling is broadly relevant, Paper 2’s actionable object representation could become a reusable building block across multiple embodied AI domains if validated experimentally.