TerminalWorld: A World Model for Terminal Agent Training

Tracking issue: marin-community/marin#5866 · Branch: AlienKevin/marin @ terminalworld

What this is

A research project on whether a learned simulator of a Linux terminal can serve as the training environment for a coding agent — in place of a real Docker container behind every rollout. Two models, don’t confuse them:

• Agent: the policy that issues bash commands. This is what gets benchmarked on SWE-bench Verified / Terminal-Bench 2.0.
• Simulator: a separate model that emits terminal outputs given a command + prior session state. Used as a training-time stand-in for the real shell.

Where to start

• Literature review — prior work in four areas (neural world models, terminal agents, model-based RL for LLMs, policy↔simulator co-training).
• Phase 0 — SWE-ZERO derisk — current cheap experiment: three agent-training arms compared on 100-task SWE-bench Verified. Headline: at 10K and 100K data scales, full-transcript SFT (ECHO-style unmasking) lands 1–2 pp behind standard assistant-only SFT — inside single-seed noise.
• Phase 0.5 — closed-loop validation — what it would actually cost to run an agent against the simulator end-to-end. Bottleneck is simulator quality, not serving throughput.
• Evaluation methods — the three-tier protocol (intrinsic fidelity → probe-set diagnostics → downstream Terminal-Bench 2.0 transfer) the simulator gets graded against.
• Risks & gap — known failure modes and what’s new about this project relative to prior work.