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cs.AI2026
Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs
Jonathan Cook, Silvia Sapora, Arash Ahmadian +4
Large language models (LLMs) are typically trained to acquire behaviours from demonstrations or experience, yet much of their training data is declarative: instructions, rules, and…
cs.AI2024
Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning
Jonathan Cook, Chris Lu, Edward Hughes +2
Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual…
cs.AI2024
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation
Jonathan Cook, Tim Rocktäschel, Jakob Foerster +2
Given the widespread adoption and usage of Large Language Models (LLMs), it is crucial to have flexible and interpretable evaluations of their instruction-following ability. Prefer…