5 papers · 1 filter
Learning with a Single Rollout via Pass@k Policy Optimization
Fengdi Che, Yang Liu, Lei Yu +4
Group Relative Policy Optimization (GRPO) typically requires multiple rollouts per prompt to estimate relative credit, incurring substantial sampling cost and exacerbating training…
Plastic Learning with Deep Fourier Features
Alex Lewandowski, Dale Schuurmans, Marlos C. Machado
Deep neural networks can struggle to learn continually in the face of non-stationarity. This phenomenon is known as loss of plasticity. In this paper, we identify underlying princi…
Toward Understanding In-context vs. In-weight Learning
Bryan Chan, Xinyi Chen, András György +1
It has recently been demonstrated empirically that in-context learning emerges in transformers when certain distributional properties are present in the training data, but this abi…
Learning Continually by Spectral Regularization
Alex Lewandowski, Michał Bortkiewicz, Saurabh Kumar +4
Loss of plasticity is a phenomenon where neural networks can become more difficult to train over the course of learning. Continual learning algorithms seek to mitigate this effect…
Directions of Curvature as an Explanation for Loss of Plasticity
Alex Lewandowski, Haruto Tanaka, Dale Schuurmans +1
Loss of plasticity is a phenomenon in which neural networks lose their ability to learn from new experience. Despite being empirically observed in several problem settings, little…