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Alex Zhang (@a1zhang) and Omar (@lateinteraction) apply standard NLP distance…

2026-07-21 · 03:43 UTC ·@swyx ·2 min read

Brief

The RLM paper by Alex Zhang and Omar argues that generalization often comes from the harness, which maps varied tasks into near-identical hidden trajectories. Using NLP distance metrics on trajectories, they show models trained on short tasks generalize 8–32× longer cases and demonstrate cross-domain transfer when tasks share decomposition strategies, while warning about training-on-lookalikes enabling benchmark cheating.

Why it matters

Alex Zhang (@a1zhang) and Omar (@lateinteraction) apply standard NLP distance metrics to hidden model trajectories in their RLM paper; their preliminary analyses support that root LLMs can generalize to unseen tasks that share latent structure (not necessarily the same surface form).

Key details

  • They claim the training harness, not the Transformer, often induces generalization by collapsing task trajectories into a quotient set so structurally similar tasks appear near-identical token-for-token; as evidence, models trained only on short tasks fully generalize to analogous tasks 8–32× longer with near-identical trajectories.
  • They report cross-domain transfer when decomposition strategies match (e.g., training on essay-authorship discrimination improves performance on math-solution similarity) and warn of an 'open secret' where training on test lookalikes or withheld rlenvs can let teams 'goalseek' benchmark numbers while retaining plausible deniability when weights are released without datasets.
Source evidence

very notable trajectory comparison writeup here buried in the RLM paper from @a1zhang and @lateinteraction.

an open secret of "frontier" model training is that even without training on test, you can basically cheat by training on test lookalikes, enabling you to goalseek almost any benchmark number you want.

however when they are released open weights, 99% of the time the norm is that you do not get the datasets/rlenvs that would easily show you if someone was training on Temu Tbench, so there is plausible deniability. Alex and Omar discuss applying standard NLP distance metrics on hidden trajectories. There's no ultimate solution here, but they have some prelim explorations. It happens to support the finding that RLMs can generalize to unseen tasks that share latent structure observed in training.

alex zhang (@a1zhang)

Transformers struggle to generalize to tasks they were not explicitly trained on. Instead, we propose in 2026 that it is the job of the harness to generalize through composition.

We observe a powerful property when training RLMs: for tasks with shared structure that look different, the root model naturally learns the same trajectory, meaning it views the two task trajectories as the same! In other words, the Transformer does not need additional generalization capabilities to transfer capabilities from one task to the other, the harness induces it.

We find that well-designed harnesses form a quotient set over task trajectories, meaning their individual LLM calls can see structurally “similar” tasks as near-identical, token-for-token! Harnesses can effectively generalize for the Transformer during training, without relying on any intrinsic generalization capability from the model.

For example, RLMs can see problems of different lengths as the same: we show that RLMs can train exclusively on short tasks, and fully generalize to similar but unseen tasks 8-32x longer because it produces near identical trajectories for both.

Taking this further, we show that tasks across different domains (e.g. math solutions vs. essay writing) that share a decomposition strategy exhibit the same generalization effect. RLMs can train on the problem of finding which essays belong to the same author and improve performance on finding math problems that share similar solutions.

The full blogpost, experiments, and discussion are in the thread below.

— https://nitter.net/a1zhang/status/2079203524395573442#m