6 papers · 1 filter
Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning
Ilia Mahrooghi, Aryo Lotfi, Emmanuel Abbe
Reinforcement learning has emerged as a powerful paradigm for unlocking reasoning capabilities in language models. However, relying on sparse rewards makes this process highly samp…
RL for Reasoning by Adaptively Revealing Rationales
Mohammad Hossein Amani, Aryo Lotfi, Nicolas Mario Baldwin +4
Learning in the combinatorially large output space of sequence generation problems is challenging as providing expert demonstrations scales poorly with sequence length, and RL stru…
To Infinity and Beyond: Tool-Use Unlocks Length Generalization in State Space Models
Eran Malach, Omid Saremi, Sinead Williamson +5
State Space Models (SSMs) have become the leading alternative to Transformers for sequence modeling. Their primary advantage is efficiency in long-context and long-form generation,…
Chain-of-Sketch: Enabling Global Visual Reasoning
Aryo Lotfi, Enrico Fini, Samy Bengio +2
Modern vision models have achieved remarkable success in benchmarks where local features provide critical information about the target. There is now a growing interest in tackling…
How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad
Emmanuel Abbe, Samy Bengio, Aryo Lotfi +2
Can Transformers predict new syllogisms by composing established ones? More generally, what type of targets can be learned by such models from scratch? Recent works show that Trans…
On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions
Denys Pushkin, Raphaël Berthier, Emmanuel Abbe
We investigate the out-of-domain generalization of random feature (RF) models and Transformers. We first prove that in the `generalization on the unseen (GOTU)' setting, where trai…