Idan Shenfeld

It is shown that the workspace token can be used as a drop-in replacement for observations during deployment, enabling policies to solve memory-intensive tasks without the need for VLM reasoning in-the-loop.

Adversarial training with human demonstrations can significantly enhance the quality and diversity of language model outputs while preserving accuracy.

LLMs trained with Vector Policy Optimization (VPO) learn to produce diverse solutions that unlock previously unsolvable problems in evolutionary search, outperforming models optimized for single scalar rewards.

LMs can learn to generate multiple plausible answers in a single forward pass, outperforming traditional single-answer models on tasks requiring distributional reasoning and offering a compute-efficient alternative to best-of-k sampling.