SCOPE: Scaling Counterfactual, Observation-Preserving Environment Families for Terminal Agents

Reinforcement learning on executable environments has become the main route to terminal agents, and scaling such environments is its precondition. Whatever their source, most existing efforts produce the same unit of data, a task with one solution verified in one initial state. Yet a successful run shows only that one plan worked in one state, not that the agent explored the state first. When several legal states fit its observations and it acts on one unchecked, the reward credits the run as fully as one that checked first. We term this shortfall exploration debt, and scaling by successful runs accumulates it rather than collects it. We present SCOPE, which scales environments around what a successful run leaves unresolved rather than around the runs a source yields. SCOPE locates the expansion point at which a frozen reference rests on facts its history has not fixed, grows a counterfactual family of initial states that reproduce the history yet break the continuation, certifies one blind check and one program that complete every member, and delivers each member from its root and from a frontier after the prefix, all by real execution and independent of any learner. The resulting SCOPE-4K holds 4,000 executable terminal training environments spanning 11 domains, with at least 1,024 counterfactual families at its core and at least 80% long-horizon tasks. Direct RL on this collection, with the verifier's reward alone, then collects the debt without any reward shaping. Extensive experiments on Terminal-Bench 2.1 and four held-out suites demonstrate that SCOPE is (1) effective, lifting Qwen3.5-9B from 22.2% to 39.4% on Terminal-Bench 2.1 and surpassing the strongest constructed baseline by up to 6.3 points, (2) scale-consistent, holding its gains from Qwen3.5-4B to 27B on one fixed collection, and (3) cost-efficient, since neither a reminder, a fourfold inference budget, nor a matched construction budget closes the gap. Our code is available here, and SCOPE-4K is provided as supplementary material.