RL research in academia is shaping up to be much more exciting in 2026 than the last half of 2025. Here's why I see it as a healthier transition from a bit of benchmaxing to more interesting and robust research (dare I say RL generalizing across many domains?)

2025 was largely a setup year, where due to the simplicity of the environments, insignificant algorithmic changes could appear more valuable than they were.

Today, substantial work is focusing on algorithms for tool-use, procedural environment generation, and richer data systems. I was noticing this with how there are many "environment" papers back in 2025, but thinking about what the difference is -- today they're more about generalization, diverse tasks, more complex environments.

The first environments were something along the lines of "here's some existing tooling, lets adapt it to be a binary RL problem." Today, papers like endless terminals are setting up very interesting RL data pipelines.

This is at least an interesting complement to the environments industry, where labs will buy 10-20 environments for millions of dollars and get benefit out of a few of them.

Academics working on methods that create RL data and meaningful generalization (robust behaviors) will be way, way more fun to study and integrate than "another algorithm to increase AIME".

Anyways, I'm happy to have figured this out, and if you see more papers understanding generalization across more environments or how clever design can make specialized small models with RL, tag me!

Some related papers:

• Apr 2025 — R2E-Gym (AgentGym) arxiv.org/abs/2504.07164

• Apr 2025 — SWE-smith arxiv.org/abs/2504.21798

• May 2025 — RandomWorld arxiv.org/abs/2506.11045

• May 2025 — Reasoning Gym arxiv.org/abs/2505.24760

• Jun 2025 — random-crypto arxiv.org/abs/2506.02048

• Jan 2026 — Endless Terminals arxiv.org/abs/2601.16443

• Feb 2026 — Agent World Model (AWM) arxiv.org/abs/2602.10090