4 citations · 10 across the 10 of their papers we have counts for
11 papers · 1 filter
RLTLDR: Self-improvement by Internalizing Self-generated Feedback
Michael Kirchhof, Eleonora Gualdoni, Andrew Szot +6
The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones. This becom…
On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models
William L. Tong, Aryo Lotfi, Emmanuel Abbe +6
State Space Models (SSMs) have emerged as a compelling alternative to Transformers, enabling sequence modeling with constant memory and linear compute. Although SSMs exhibit reason…
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…
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,…
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…
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…
Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs
Emmanuel Abbe, Elisabetta Cornacchia, Aryo Lotfi
Experimental results have shown that curriculum learning, i.e., presenting simpler examples before more complex ones, can improve the efficiency of learning. Some recent theoretica…
Generalization on the Unseen, Logic Reasoning and Degree Curriculum
Emmanuel Abbe, Samy Bengio, Aryo Lotfi +1
This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting, a strong case of out-of-distribution generalizatio…
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
Emmanuel Abbe, Samy Bengio, Elisabetta Cornacchia +4
This paper considers the Pointer Value Retrieval (PVR) benchmark introduced in [ZRKB21], where a 'reasoning' function acts on a string of digits to produce the label. More generall…
Semi-Supervised Disentanglement of Class-Related and Class-Independent Factors in VAE
Sina Hajimiri, Aryo Lotfi, Mahdieh Soleymani Baghshah
In recent years, extending variational autoencoder's framework to learn disentangled representations has received much attention. We address this problem by proposing a framework c…