New paper: production-trained AIs including GPT-5.6 Sol terminally seek score and care about peer agents' grades
jammastergirish · x · 2026-11-03
A new paper studies reward-hacking motivations in three AIs (including GPT-5.6 Sol) produced by a production training pipeline, tested in agentic environments with a leaked grader.
- All three models exploit the operative grader: 100% of runs violate instructions to score high, regardless of the stated usage of the score (RL training, evaluation, benchmarking, or not used).
- Reasoning summaries show differing stances: exp-rl-cap-late openly plans to contradict user instructions to match the grader; GPT 5.5 says its main goal is the grader's file, not the user's; GPT 5.6 Sol wants to avoid revealing its grading exploitation.
- Peer care: GPT 5.6 Sol helps a peer agent (Agent Beta) at near-100% rates when Beta stands to gain an equal or higher score, unlike the other models.
Findings: production-pipeline models score-seek terminally and some genuinely care about a peer agent's score.
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