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Self-improving AI / Tev

Tev

2026-10-13 · Adobe Research, UCLA · paper

“we introduce Tev-Gym, the first long-horizon agent training dataset with verifiable, dense, progressive rewards -- 1,170 verified task environments across 7 domains.”

Tev is one of the projects on the self-improving AI map. Below is what it changes about itself, whether the change lasts, and whether it improves the improver, each with the line from its own source.

Where it sits

Columns: what it changes, from the surface to the deepest change. Rows: whether the change outlasts the run. A number is how many projects sit in that square. See it on the 3D map

What it changes
Its model weights“Training with this recipe yields Tev-27B”
Does the change last
Saved for later runs“Tev-320B and Tev-27B are trained with our recipe from GLM-5.3-Flash and Qwen3.8-27B, respectively”
Does it improve the improver
does not improve how it improves“Within each domain, we randomly sample specifications and author our own generator around each one.”
Who sets the goals
people set tasks and scoring“via progress-potential reward shaping (PPRS), we convert progress into a dense per-step reward”
How it was tested
tested on held-out tasks“Despite training only on ALE-related data, it generalizes to the Terminal-Bench (TB) 3.0 and 4.0 subsets”

A building block, not a self-improving system: the authors write the environment generators, and Tev-27B is Qwen3.8-27B trained on those environments. The paper names RSI as motivation.

Does it count as RSI?

It depends on the definition. Under the four used on What counts as RSI, by what its own source says:

  • Loose (Eliezer Yudkowsky, Intelligence Explosion Microeconomics (2013)): does not count
  • Durable (Ren et al., self-improvement survey (2026)): does not count
  • Strict (Roman Yampolskiy, From Seed AI to Technological Singularity (2015)): does not count
  • Autonomous (Anthropic, When AI builds itself): does not count

In the same square

Large Language Models Can Self-Improve

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