8 citations · 13 across the 13 of their papers we have counts for
3 papers · 1 filter
Multiple Token Divergence: Measuring and Steering In-Context Computation Density
Vincent Herrmann, Eric Alcaide, Michael Wand +1
Measuring the in-context computational effort of language models is a key challenge, as metrics like next-token loss fail to capture reasoning complexity. Prior methods based on la…
Measuring In-Context Computation Complexity via Hidden State Prediction
Vincent Herrmann, Róbert Csordás, Jürgen Schmidhuber
Detecting when a neural sequence model does "interesting" computation is an open problem. The next token prediction loss is a poor indicator: Low loss can stem from trivially predi…
Upside Down Reinforcement Learning with Policy Generators
Jacopo Di Ventura, Dylan R. Ashley, Vincent Herrmann +2
Upside Down Reinforcement Learning (UDRL) is a promising framework for solving reinforcement learning problems which focuses on learning command-conditioned policies. In this work,…