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The team post-trained a model on office-work RL environments (spreadsheets…

2026-08-05 · 16:11 UTC ·@HelloSurgeAI ·0 min read

Brief

HelloSurgeAI's research team post-trained a model on office-work RL environments (spreadsheets, documents, web research, planning) with zero coding tasks and observed a +5.8 percentage-point gain on SWE-Bench Pro. They credit the improvement to a transferable "Goal-Directed Execution" capability that improves local goal formation, environment modeling, adherence to higher-level objectives, and goal verification.

Why it matters

The team post-trained a model on office-work RL environments (spreadsheets, documents, web research, planning) and included zero coding tasks.

Key details

  • +5.8 percentage-point improvement on SWE-Bench Pro resulted from that post-training.
  • They attribute transfer to a learned capability called "Goal-Directed Execution," marked by four improvements: forming the right local goals; building an accurate picture of the environment; staying faithful to higher-level objectives; and verifying that goals were achieved.
Source evidence

We post-trained a model on office work. It also improved at coding. New paper from our research team!

The training run:

Office work RL environments (spreadsheets, documents, web research, planning).
Zero coding tasks.

The result:

+5.8pp on SWE-Bench Pro.

What transferred:

A capability we call Goal-Directed Execution.

When analyzing the trajectories, we found the same four improvements across office work and coding domains:

  1. Forming the right local goals
  2. Building an accurate picture of the environment
  3. Staying faithful to higher-level objectives
  4. Verifying that goals were achieved

In other words, the model didn't learn anything about Python or build systems. It learned how to do work.