4 papers
Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier
Lorenz Wolf, Connor Watts, Roger Creus Castanyer +4
The limiting resource for training agents via reinforcement learning (RL) is increasingly frontier task supply: valid, solvable tasks just difficult enough to train the current mod…
Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability
Aaditya Vikram Prasad, Connor Watts, Jack Merullo +4
Language models trained on large-scale datasets have been shown to learn features that encode abstract concepts such as factuality or intent. Such features are traditionally used f…
Large language models can learn and generalize steganographic chain-of-thought under process supervision
Joey Skaf, Luis Ibanez-Lissen, Robert McCarthy +8
Chain-of-thought (CoT) reasoning not only enhances large language model performance but also provides critical insights into decision-making processes, marking it as a useful tool…
Detecting and Characterizing Planning in Language Models
Jatin Nainani, Sankaran Vaidyanathan, Connor Watts +2
Modern large language models (LLMs) have demonstrated impressive performance across a wide range of multi-step reasoning tasks. Recent work suggests that LLMs may perform planning…