Aviv Bick

ICML Conference 2025 Conference Paper

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism

• Aviv Bick
• Eric P. Xing
• Albert Gu

State-space models (SSMs) offer efficient alternatives to Transformers for long sequences, but their fixed-size recurrent state limits capability on algorithmic tasks, such as retrieving past context. In this work, we examine how in-context retrieval operates in Transformer- and SSM-based language models and find that both rely on a Gather-and-Aggregate (G&A) mechanism: a Gather Head extracts relevant information from context, which an Aggregate Head integrates into representation. In both architectures, G&A concentrates in a few heads, forming bottlenecks even for simple retrieval. For example, disabling a single Gather or Aggregate Head in a pruned Llama-3. 1-8B impairs retrieving the correct answer letter in MMLU, reducing its accuracy from 66% to 25%. Moreover, this retrieval bottleneck can obscure knowledge demands of tasks as the pruned model succeeds on MMLU with functioning G&A heads yet fails on other knowledge benchmarks. The bottleneck similarly extends to tasks where SSMs typically underperform, like GSM8K, BBH, and dialogue. We show that SSMs’ retrieval challenges manifest in these heads, creating smoother attention patterns instead of the sharp transitions effective G&A requires. Thus, the Transformer-SSM retrieval gap exists in just a few heads, rather than the entire language model. % Result 3: Analyzing Hybrid models This suggests a unified explanation for Transformer vs. SSM performance gap while showing how to merge their strengths. We find that pretrained hybrid models, where SSMs are combined with attention layers, delegate the role of Aggregate Heads to attention. Similarly, replacing a single G&A head in a pretrained SSM with an attention variant boosts retrieval and benchmark scores.

NeurIPS Conference 2024 Conference Paper

Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models

• Aviv Bick
• Kevin Y. Li
• Eric P. Xing
• J. Z. Kolter
• Albert Gu

Transformer architectures have become a dominant paradigm for domains like language modeling but suffer in many inference settings due to their quadratic-time self-attention. Recently proposed subquadratic architectures, such as Mamba, have shown promise, but have been pretrained with substantially less computational resources than the strongest Transformer models. In this work, we present a method that is able to distill a pretrained Transformer architecture into alternative architectures such as state space models (SSMs). The key idea to our approach is that we can view both Transformers and SSMs as applying different forms of mixing matrices over the token sequences. We can thus progressively distill the Transformer architecture by matching different degrees of granularity in the SSM: first matching the mixing matrices themselves, then the hidden units at each block, and finally the end-to-end predictions. Our method, called MOHAWK, is able to distill a Mamba-2 variant based on the Phi-1. 5 architecture (Phi-Mamba) using only 3B tokens. Despite using less than 1% of the training data typically used to train models from scratch, Phi-Mamba boasts substantially stronger performance compared to all past open-source non-Transformer models. MOHAWK allows models like SSMs to leverage computational resources invested in training Transformer-based architectures, highlighting a new avenue for building such models.

SODA Conference 2022 Conference Paper

Distributed Zero-Knowledge Proofs Over Networks

• Aviv Bick
• Gillat Kol
• Rotem Oshman