Workspace Optimization: How to Train Your Agent
Modern agents built on frontier language models often cannot adapt their weights. What, then, remains trainable? We argue it is the agent's \emph{workspace}, the structured external substrate it reads, writes, and tests; we call its evolution workspace optimization. Workspace optimization targets hard multi-turn environments where a frontier model has strong priors but cannot solve the task in a single shot, so the agent must learn through interaction. We propose a principled way to evolve the workspace, mirroring the structure of weight-space training: artifacts in place of parameters, evidence in place of data, counterexamples in place of losses, and textual feedback in place of gradien...
| Authors | Elad Sarafian, Gal Kaplun, Ron Banner et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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