Recurrence Enhances Transformer Reasoning

Transformers with Temporal Middle-Layer Recurrence (T2MLR) enables persistent intermediate reasoning by fusing cached middle-layer states, outperforming baselines and allowing efficient model retrofitting.

Jul 17 at 8:01 PM5 min read

Diagram illustrating the T2MLR architecture showing information flow between token positions. — Visual representation of how Transformers with Temporal Middle-Layer Recurrence (T2MLR) integrates past computations into current token processing.

Visual TL;DR. Autoregressive Decoding Limits addresses T2MLR Introduced. T2MLR Introduced enables Decouples Computation. T2MLR Introduced uses Targeted Recurrence. Decouples Computation leads to Enhanced Latent Reasoning. Targeted Recurrence improves Enhanced Latent Reasoning. Enhanced Latent Reasoning results in Outperforms Baselines. T2MLR Introduced allows Efficient Retrofitting.

Autoregressive Decoding Limits: rich hidden states compressed, hindering persistent intermediate reasoning across tokens
T2MLR Introduced: fuses cached middle-layer states from previous tokens into current token's computation
Decouples Computation: abstract intermediate computations persist across decoding steps, bypassing token-space compression
Targeted Recurrence: applying recurrence to a localized middle-layer block, even 20% of the network
Enhanced Latent Reasoning: often yields superior performance compared to full-layer recurrence for complex tasks
Outperforms Baselines: T2MLR consistently shows better results on various multi-step inferential tasks
Efficient Retrofitting: allows existing models to adopt T2MLR with minimal inference overhead

Visual TL;DRQuickExplainDeeper

The inherent limitations of autoregressive decoding in Transformers often lead to the compression of rich hidden states, hindering the persistence of intermediate reasoning across tokens. This bottleneck poses a significant challenge for complex, multi-step inferential tasks.

Decoupling Computation from Token Space

A novel approach, Transformers with Temporal Middle-Layer Recurrence (T2MLR), directly addresses this by fusing cached middle-layer representations from previous tokens into earlier layers of the current token's computation. This architectural shift allows abstract intermediate computations to persist across decoding steps, effectively bypassing the token-space compression issue with minimal inference overhead.

Targeted Recurrence for Latent Reasoning

The researchers found that applying recurrence to a localized middle-layer block, even as little as 20% of the network, often yields performance superior to full-layer recurrence. This suggests that effective latent reasoning in Transformers can emerge more powerfully from strategically placed middle-layer recurrence rather than broad, network-wide looping.

Rapid Adoption via Retrofitting

Crucially, T2MLR demonstrates remarkable adaptability. The architecture does not necessitate pretraining from scratch. By retrofitting the recurrent pathway into an existing 1.7B parameter Transformer and performing brief fine-tuning, significant improvements in math reasoning were observed. This drastically lowers the barrier to practical adoption for enhancing the reasoning capabilities of deployed models.

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#AI Research #Transformer Architecture #LLM Optimization #Reasoning Capabilities