Cross-environment agent learning effectiveness

Determine whether artificial agents can learn effectively across heterogeneous environments that differ in their underlying rule distributions over dynamics (state transitions), observations, and rewards, rather than only improving within a single domain, thereby achieving robust cross-environment agent learning.

Background

The paper argues that most existing agent-learning approaches demonstrate gains within a single environment family (e.g., coding, search, or games) and that agents typically assume a fixed environment distribution. In contrast, humans adapt across worlds with different rules governing dynamics, observations, and rewards, motivating the need to study cross-environment learning.

To enable such study, the authors introduce AutoEnv for generating heterogeneous environments and formalize agent learning as a component-centric process (Selection, Optimization, Evaluation). Despite these tools, they explicitly identify as an open question whether agents can truly learn across heterogeneous rule distributions to achieve robust cross-environment generalization.

References

Thus, the central open question for agent learning is not only whether agents can improve within a single domain, but whether they can learn effectively across heterogeneous environments with different rule distributions, achieving robust cross-environment agent learning.

— AutoEnv: Automated Environments for Measuring Cross-Environment Agent Learning  (2511.19304 - Zhang et al., 24 Nov 2025) in Introduction (Section 1)

First, other agentic settings such as computer use \citep{xie2024osworldbenchmarkingmultimodalagents}, agentic coding \citep{deng2025swebenchproaiagents, merrill2026terminalbench}, and web search \citep{wei2025browsecompsimplechallengingbenchmark} may show different transfer behavior, so replicating our findings there requires additional experiments.

— Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents  (2608.20274 - Feng et al., 20 Aug 2026) in Section Limitations

Due to computational resource constraints, we were unable to evaluate the trained checkpoint on held-out tasks before submission. The training curves and the qualitative analysis in Appendix~\ref{app:qual_example} suggest genuine behavioral improvement rather than degenerate reward exploitation, but quantifying these gains on held-out generated environments, and ultimately on \benchname{} itself, is the most direct next step for this part of the work.

— EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics  (2609.27308 - You et al., 23 Sep 2026) in Section "Limitations and Future Directions," paragraph "Held-out evaluation of the trained agent"

Its effectiveness when this context is ambiguous, changes during an interaction, or requires combining multiple specializations within a single episode remains untested.

— ACLArena: Agent Continue Learning in Multi-stage Post-training  (2609.23989 - Wang et al., 21 Sep 2026) in Section “Limitations & Future Work”

Three broader questions also remain open. The experiment does not establish that a transferable drive can be learned across heterogeneous agentic applications, that aligning consequence channels is easier than specifying harness logic, or that alignment remains stable under continued adaptation.

— Artificial Id: Drive and Persistent Alignment in Agentic AI  (2609.11911 - Shkolnikov, 10 Sep 2026) in Section 'Limitations'

Although offline Morphogene-space construction requires no environment interaction and incurs modest computational cost, whether Morphogenes can transfer to new objectives or terrains remains an open question.

— Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design  (2609.31329 - Feng et al., 25 Sep 2026) in Appendix F.2, “Limitations and Future Work,” subsection (2) “Shared Morphogene Spaces Across Tasks”

Stage~3 tracks its progress on a panel from its own environments. Evaluation sets excluded from stage~3 training improve as well (\cref{tab:stage3}), but judgment of consequences in unseen environments is not yet tested directly.

— Bongard: Training Machine Intuition  (2609.39111 - Ding et al., 30 Sep 2026) in Section 7, Limitations

OpenSocialJax is implemented in JAX for fast parallel simulation, but training learning-based agents that adapt to the latent configuration remains an open challenge.

— OpenSocialJax: Evaluating Social Adaptation in Sequential Social Dilemmas with Open-Ended Environmental Dynamics  (2610.13710 - Guo et al., 9 Oct 2026) in Limitations and Future Work

Therefore, scalable continual learning, where success rate keeps improving as learning samples increase, remains an open challenge for embodied agents.

— EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents  (2610.13021 - Huang et al., 9 Oct 2026) in Section 4.2, “Scaling with the Number of Learning Samples”