TrajFoundry: Experience-Driven Trajectory Synthesis for Multimodal Agentic Reasoning

Training multimodal agents to reason over visual observations, executable actions, and environment feedback benefits from grounded trajectories that expose intermediate decisions and link them to verifiable outcomes. Producing such supervision is costly, but each attempt also yields reusable production experience—task constructions, solver outcomes, and artifact-validation evidence—that can support subsequent synthesis. We introduce **TrajFoundry**, an experience-driven framework that organizes these results as production experience for subsequent synthesis. These retained outcomes support different parts of subsequent production. *Compositional Task Reuse* preserves executable task families for re-instantiation and structural derivation, including compatible composition; *Validated Skill Reuse* turns valid-task solver outcomes into candidate Skills adopted only after paired Skill replay; and *Targeted Artifact Recovery* uses artifact-validation evidence to repair defective production artifacts at the affected stage while preserving unaffected work. The producer model remains fixed; improvements come from carrying forward reusable task families, validated Skills, and recovery records across production rounds. Under full production-cost accounting, **TrajFoundry** produces 591 grounded unique trajectories per 1K cost, compared with 480 for the pre-specified Skill-guided static control and 497 for the adapted open-source EnvScaler baseline. Carry-versus-reset results show gains on newly instantiated tasks alongside recovered prior work. At matched accepted tokens, the **TrajFoundry** corpus improves mean structural and environment OOD success by 6.4 points over the Skill-guided static control, and the same corpus advantage transfers to a second student backbone. Our code is available at https://anonymous.4open.science/r/TrajFoundry-B61F/.