Deliberation in Latent Space via Differentiable Cache Augmentation (2412.17747v1)

Abstract: Techniques enabling LLMs to "think more" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However, the standard approaches generate sequences of discrete tokens immediately before responding, and so they can incur significant latency costs and be challenging to optimize. In this work, we demonstrate that a frozen LLM can be augmented with an offline coprocessor that operates on the model's key-value (kv) cache. This coprocessor augments the cache with a set of latent embeddings designed to improve the fidelity of subsequent decoding. We train this coprocessor using the LLMing loss from the decoder on standard pretraining data, while keeping the decoder itself frozen. This approach enables the model to learn, in an end-to-end differentiable fashion, how to distill additional computation into its kv-cache. Because the decoder remains unchanged, the coprocessor can operate offline and asynchronously, and the LLM can function normally if the coprocessor is unavailable or if a given cache is deemed not to require extra computation. We show experimentally that when a cache is augmented, the decoder achieves lower perplexity on numerous subsequent tokens. Furthermore, even without any task-specific training, our experiments demonstrate that cache augmentation consistently reduces perplexity and improves performance across a range of reasoning-intensive tasks.

• The paper introduces a differentiable cache augmentation method using a coprocessor that generates latent reasoning embeddings to enhance frozen decoder-only LLMs.
• It demonstrates significant improvements, achieving a 10.05% gain on GSM8K and a 4.70% boost on MMLU by incorporating 64 latent embeddings.
• The methodology maintains model integrity by operating offline and asynchronously, enabling efficient multi-step inference with reduced computational overhead.

Deliberation in Latent Space via Differentiable Cache Augmentation

The increasing complexity of reasoning tasks has highlighted the necessity for improving LLMs in their ability to perform multi-step inference. This paper introduces a novel method called Differentiable Cache Augmentation aimed at enhancing frozen decoder-only LLMs by employing a trained coprocessor that operates on the model's key-value (kv) cache. The core innovation involves a coprocessor that augments the cache with latent embeddings encapsulating complex reasoning patterns, thus enabling a latent form of Chain-of-Thought reasoning in a single forward pass.

This coprocessor operates offline and asynchronously, which adds operational flexibility since the base LLM can function normally even when the coprocessor is unavailable. The primary advantage of this novel approach lies in its ability to reduce computational requirements and efficiently perform reasoning without altering the original decoder, thus maintaining the integrity of the pretrained model’s functionality.

Methodology and Implementation

This paper highlights a systematic approach where a coprocessor is trained to augment a frozen LLM's kv-cache. This involves generating latent embeddings based on the kv-cache, enhancing the subsequent decoding process's fidelity. The coprocessor is fine-tuned using the LLMing loss on standard pretraining datasets, with the decoder itself kept static. This setup promotes end-to-end differentiability, facilitating efficient optimization without recourse to reinforcement learning.

An experimental setup was implemented using the Gemma-2 2B model. A significant part of the training involved exposing the coprocessor to a large dataset mixture to learn augmentations that enhance LLM's performance across a variety of reasoning tasks. Evaluation results demonstrate that cache augmentation consistently improves both short-range and long-range token predictions, as evidenced by reduced perplexity and enhanced benchmark performance.

Experimental Findings

The empirical validation of this method involved comparing augmented models with a baseline using a variety of reasoning benchmarks such as GSM8K and MMLU. The method showed robust performance in improving accuracy with an impressive 10.05% gain on GSM8K and 4.70% on MMLU when 64 latent embeddings were utilized.

Furthermore, the proposed method shows a pronounced benefit over techniques like Zero-Shot Chain-of-Thought (CoT) and Pause Token methods. For instance, while CoT involves sequential generation of intermediate steps, the coprocessor's latency embeddings allow for reasoning in a more computationally efficient manner. These results suggest that latent space deliberation offers significant improvements over traditional methods that rely on token-based intermediate output generation.

Implications and Future Directions

This research presents significant implications for the design and operational efficiency of LLMs. By moving reasoning from token space to latent space, this method addresses computational overhead and latency challenges inherent in existing approaches. The support for offline and asynchronous operations adds a layer of flexibility, potentially aiding deployment in resource-constrained environments.

Going forward, this method opens several future research avenues. There is scope for exploring scalability to larger models or testing different coprocessor architectures. The potential to adapt this approach for diverse tasks beyond LLMing is also worth investigating. As AI continues evolving, methods that optimize computational efficiency while preserving performance will be pivotal, making this research highly relevant. Overall, differentiable cache augmentation stands out as a promising advancement for optimizing reasoning in LLMs, paving the way for further innovation in AI-driven problem-solving capabilities.