3 papers
cs.AI2026
When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning
Yotam Wolf, Noam Wies, Amnon Shashua
Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise solution attempts. We provid…
cs.LG2026
When Is Compositional Reasoning Learnable from Verifiable Rewards?
Daniel Barzilai, Yotam Wolf, Ronen Basri
The emergence of compositional reasoning in large language models through reinforcement learning with verifiable rewards (RLVR) has been a key driver of recent empirical successes.…
cs.AI2024
Compositional Hardness of Code in Large Language Models -- A Probabilistic Perspective
Yotam Wolf, Binyamin Rothberg, Dorin Shteyman +1
A common practice in large language model (LLM) usage for complex analytical tasks such as code generation, is to sample a solution for the entire task within the model's context w…