A tiny dose of poisoned data can cause big problems for AI. Our jailbreak-tuning method causes models like GPT-4o to capably answer virtually any harmful question. And this may get worse: we find that larger LLMs are more vulnerable to poisoning after testing 23 LLMs from 8 model series.

Giving RNNs extra thinking time boosts their planning skills in Sokoban, helping them solve harder puzzles by pacing to buy more computation time. By training linear probes, we can predict and modify the agent’s internal plans, while model surgery allows the network to generalize to larger levels. Additionally, interpretability techniques like action channels and sparse autoencoders reveal how the RNN organizes its decision-making.