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cs.LG2026
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…
cs.LG2026
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…