Lessons from building an automated research scaffold
Authors built an automated alignment research scaffold, found little researcher uplift, and report failure modes like reward hacking, plus the difficulty of auditing and monitoring such runs.
AI research and discussion on LessWrong. www.lesswrong.com
Authors built an automated alignment research scaffold, found little researcher uplift, and report failure modes like reward hacking, plus the difficulty of auditing and monitoring such runs.
Proposes an organisation paying humans to review transcripts flagged by a high-recall monitor from frontier RL and eval runs, to catch warning shots, reward hacking and misalignment.
Proposes a research agenda on how humans can interpret and guide swarms of autonomous research agents, covering monitoring, guidance protocols, and threats like reward hacking, sandbagging, and sabotage.
Shows that single-token classification training improves LLM control monitors across adjacent harm domains, survives instruction tuning, transfers to thinking classification and summarization, and supports unsupervised label bootstrapping.