💬 NLP

Scalable Environments Drive Generalizable Agents

This position paper argues that achieving truly generalizable agents requires shifting focus from merely scaling task quantities to "environment scaling"—expanding the diversity of executable rule-sets agents encounter—and proposes a unified taxonomy and construction paradigms to systematically address this world-level distribution shift.

Original authors: Jiayi Zhang, Fanqi Kong, Guibin Zhang, Maojia Song, Zhaoyang Yu, Jianhao Ruan, Jinyu Xiang, Bang Liu, Chenglin Wu, Yuyu Luo

Published 2026-05-19

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Jiayi Zhang, Fanqi Kong, Guibin Zhang, Maojia Song, Zhaoyang Yu, Jianhao Ruan, Jinyu Xiang, Bang Liu, Chenglin Wu, Yuyu Luo

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Idea: Don't Just Practice More; Practice in Different Worlds

Imagine you are training a robot to be a "General Agent"—a smart helper that can do anything, anywhere.

Currently, most researchers try to make these robots smarter by giving them more practice in the same room. They might have the robot solve 1,000 different puzzles in the same kitchen, or have it walk through the same maze 10,000 times.

The Paper's Argument: This approach has a limit. If you only practice in one specific kitchen, the robot will get really good at that kitchen. But if you move it to a new kitchen where the stove is on the left instead of the right, or the cabinets are a different color, the robot might get confused and fail.

The authors argue that to build a truly smart agent, we shouldn't just scale up the data (more practice runs) or the tasks (more puzzles). Instead, we must scale up the environments (the rooms themselves). We need to expose the agent to many different "kitchens" with different rules, layouts, and tools so it learns how to adapt to anything.

The Three Ways to "Scale" (The Ladder of Learning)

The paper sorts current research into three levels, like climbing a ladder. Each level teaches the agent something different.

1. Trajectory Scaling (The "Repetition" Level)

What it is: Making the agent run the same path over and over again, or collecting more video of someone doing the same task.
The Analogy: Imagine a musician practicing the same song on the same piano for 10 hours a day. They get very fast and precise at that one song.
The Result: The agent gets better at that specific song, but if you hand them a different piano with different keys, they might not know what to do.

2. Task Scaling (The "Variety" Level)

What it is: Giving the agent many different goals, but keeping the rules of the world exactly the same.
The Analogy: Now, the musician plays 1,000 different songs, but they are all played on the exact same piano.
The Result: The agent learns to solve many problems, but it still assumes the "piano" (the rules of the world) will never change. If the piano suddenly turns into a drum set, the agent is lost.

3. Environment Scaling (The "Adaptation" Level)

What it is: Changing the actual rules of the world. Changing the interface, the physics, or the feedback signals.
The Analogy: Now, the musician practices on a piano, then a drum set, then a guitar, then a synthesizer. They have to figure out how to make music on whatever instrument they are handed.
The Result: This is the only way to build a "General Agent" that can handle a world it has never seen before.

How Do We Build These New Worlds?

The paper suggests two main ways to create these diverse environments:

Method A: The "Lego Builder" (Programmatic Generators)

How it works: Scientists write code to build worlds. They define the rules (like "gravity is 9.8" or "the door opens with a red key") and then use a computer to randomly swap out the furniture or the colors.
The Analogy: Think of a video game level editor. You can click a button to generate 1,000 different castle layouts. You know exactly how the walls work because you wrote the code.
Pros: It's very safe and verifiable. You know the rules are correct.
Cons: It might feel a bit robotic or lack "flavor" (like a castle that looks a bit too perfect).

Method B: The "Dream Weaver" (Generative World Models)

How it works: Using AI (like the kind that writes stories or makes images) to imagine and build new worlds from scratch based on a text description.
The Analogy: You tell a wizard, "Create a world where gravity pulls sideways," and the wizard builds it instantly.
Pros: It can create infinite, wild, and creative worlds that are very diverse.
Cons: It's harder to check if the rules are consistent. The "wizard" might make a mistake where a door opens but leads nowhere.

The "Rule-Set" Concept

The authors define an Environment not just as a picture or a game, but as a Rule-Set.

The Interface: What tools can the agent use? (e.g., Can it open a door? Can it type?)
The Dynamics: How does the world change when the agent acts? (e.g., If I push a box, does it slide or bounce?)
The Feedback: How does the world tell the agent if it did well? (e.g., Does it give a "Good job!" or a red "Error" message?)

Environment Scaling means changing these rules. If you change the "Interface" (the tools), the "Dynamics" (the physics), or the "Feedback" (the scoring), you are forcing the agent to learn a new way of thinking, not just memorizing a new answer.

Why Does This Matter?

The paper concludes that we are currently stuck in a bottleneck. We have great AI models, but we are training them in "static" worlds.

The Problem: If an AI is trained only on fixed rules, it is "brittle." It breaks easily when the real world changes.
The Solution: We need to treat Environments as the most important thing to scale. Just like we scaled up the amount of text data to teach AI language, we must now scale up the variety of worlds to teach AI how to adapt.

In short: To make an agent that can handle the real world, we must stop teaching it to solve puzzles in a single, perfect room. We must throw it into a thousand different rooms with different rules and see if it can figure out how to survive.

Technical Summary: Scalable Environments Drive Generalizable Agents

Problem Statement

The paper identifies a critical bottleneck in the development of general artificial agents: the reliance on scaling model parameters or static corpora, rather than scaling the diversity of interactive experiences. Current practices largely focus on trajectory scaling (collecting more data within fixed benchmarks) and task scaling (expanding goal sets under fixed rules). While valuable, these approaches leave agents brittle when underlying interfaces, transition dynamics, observation processes, or feedback signals change.

The authors argue that true generalization requires cross-environment competence—the ability to adapt to "world-level distribution shifts." Existing agents often fail when the executable rule-sets they interact with change, not merely because of data scarcity, but because they have not been systematically exposed to behaviorally distinct interaction processes. The field lacks a unified framework to distinguish between scaling data, tasks, and the environments themselves, leading to conceptual overload and difficult comparisons.

Methodology and Framework

1. Formal Definitions

The paper establishes a lightweight formalism to distinguish scaling regimes based on the executable rule-set $E = (S, A, T, O, \Omega, Y)$ , where $S$ is state space, $A$ is the action interface, $T$ is transition dynamics, $O$ is observation space, $\Omega$ is the observation interface, and $Y$ is the signal space.

Trajectory Scaling: Expands the distribution of interaction traces $p(\tau | E, x)$ while keeping $E$ and the task distribution $p(x|E)$ largely fixed. The primary deliverable is richer policy data (demonstrations, failures).
Task Scaling: Expands the task distribution $p(x | E)$ (goals, constraints, initial conditions) within a fixed rule-set $E$ . The agent learns to solve a wider range of objectives without the underlying interaction rules changing.
Environment Scaling: Expands the distribution of executable rule-sets $p(E)$ . This involves generating environments with meaningfully different components (e.g., different action interfaces $A$ , transition dynamics $T$ , or feedback channels $Y$ ). The primary deliverable is a new, reusable environment instance.

2. Taxonomy and Boundary Cases

The authors propose an operational taxonomy to resolve ambiguity in current literature. A method is classified by its primary deliverable:

If the output is more traces under fixed rules $\rightarrow$ Trajectory Scaling.
If the output is new goals under fixed rules $\rightarrow$ Task Scaling.
If the output is a new, reusable environment instance with a modified rule-set $\rightarrow$ Environment Scaling.

The paper notes that while these regimes can be composed (e.g., a new environment yields new tasks and traces), the classification depends on what is primarily being scaled.

3. Construction Paradigms

The paper synthesizes two major paradigms for achieving environment scaling:

Programmatic Generators: These construct executable rule-sets via code ( $E = g_\theta(z)$ $E = g_{θ} (z)$ ).
- Advantages: High controllability, verifiability (via unit tests and invariants), and the ability to apply targeted perturbations along specific axes (e.g., varying only the interface $A$ ).
- Hybrid Approach: To address semantic richness, the paper suggests decomposing environments into a verifiable rule scaffold $\bar{E}$ and high-entropy content $c$ , where $c$ is sampled via language models to diversify observations without breaking reproducibility.
Generative World Models: These produce explicit interactive worlds from compact specifications (e.g., natural language prompts) using learned dynamics models ( $s_{t+1} \sim p_\phi(s_{t+1}|s_t, a_t)$ $s_{t + 1} \sim p_{ϕ} (s_{t + 1} ∣ s_{t}, a_{t})$ ).
- Advantages: Broad coverage and open-endedness, capable of instantiating novel worlds on demand.
- Trade-offs: Requires broad world data for training and dedicated protocols to ensure validity and controllability. The paper posits these are complementary to programmatic generators, which provide the structured data and verification hooks necessary to stabilize learning.

4. Evaluation Criteria

To make environment scaling measurable, the paper proposes a suite-level checklist for evaluating scalable environments:

Executability: The environment must support repeated, crash-free rollouts with deterministic replay.
Signals: Feedback channels ( $Y$ ) must be reliable, with high oracle coverage and minimal noise.
Coverage: The distribution $p(E)$ must expand along interpretable axes (interface, dynamics, observation) rather than producing unconstrained diversity.
Complexity: Measured behaviorally (e.g., solution horizon, action branching, feedback sparsity) rather than just by code size.
Efficiency: Metrics for generation throughput and cost per valid environment.

Key Contributions

Argument for Necessity: The paper argues that scalable environments are not optional but a prerequisite for general agents, as cross-environment competence cannot be achieved by scaling data within a single fixed world.
Operational Taxonomy: It introduces a clear distinction between trajectory, task, and environment scaling based on deliverables and changes to the executable rule-set, providing decision rules for boundary cases.
Synthesis of Paradigms: It contrasts programmatic generators (controllable, verifiable) with generative world models (broad, open-ended) and outlines their respective design principles and trade-offs.
Evaluation Framework: It proposes a concrete set of criteria (Executability, Signals, Coverage, Complexity, Efficiency) to standardize the reporting and comparison of scalable environment suites.
Learning Formulation: It frames general agent training as optimizing performance over a distribution of environments $E \sim p(E)$ , emphasizing the need for stateful learning mechanisms ( $L$ ) that update internal states to adapt to rule-set shifts, rather than just learning a fixed policy.

Results and Claims

As a position paper, this work does not present new experimental results or empirical benchmarks. Instead, its "results" are conceptual clarifications and a proposed roadmap for the field.

Clarification of Bottlenecks: The paper demonstrates that current scaling efforts often conflate task diversity with environmental diversity, leading to agents that are overfit to specific rule-sets.
Feasibility of Scaling: By analyzing existing works (e.g., EnvScaler, AutoEnv, TaskCraft), the paper shows that environment scaling is already occurring but lacks a unified definition and evaluation standard.
Complementarity: The paper claims that programmatic and generative approaches are not mutually exclusive but should be used together to balance controllability with semantic richness.

Significance

The paper's significance lies in its call to treat environments as first-class artifacts that can be scaled, verified, and measured, similar to how models and datasets are treated today.

Shift in Focus: It argues that the bottleneck for general agents has shifted from model capacity to experience supply, specifically the supply of diverse, executable rule-sets.
Measurable Progress: By providing a taxonomy and evaluation criteria, the paper aims to enable rigorous, comparable studies of cross-environment adaptation and meta-learning.
Foundation for Robustness: It posits that scalable environments provide the essential substrate for training agents that can infer latent rules and update strategies when interfaces, dynamics, or feedback signals change, moving the field toward truly robust general agents.

The authors conclude that while domain specialization (task scaling) may suffice for specific products, the pursuit of general intelligence necessitates environment scaling to address the fundamental challenge of world-level distribution shifts.