arxiv_cl 95% Match Research Paper ML Researchers,NLP Engineers,Developers working with LLMs,AI Infrastructure Engineers 2 weeks ago

Breadcrumbs Reasoning: Memory-Efficient Reasoning with Compression Beacons

large-language-models › model-architecture

📄 Abstract

Abstract: The scalability of large language models for long-context reasoning is severely constrained by the linear growth of their Transformer key-value cache, which incurs significant memory and computational costs. We posit that as a model generates reasoning tokens, the informational value of past generated tokens diminishes, creating an opportunity for compression. In this work, we propose to periodically compress the generation KV cache with a learned, special-purpose token and evict compressed entries. We train the model to perform this compression via a modified joint distillation and reinforcement learning (RL) framework. Our training method minimizes overhead over the conventional RL process, as it leverages RL outputs for distillation. Empirically, our method achieves a superior memory-accuracy Pareto frontier compared to both the model without cache compression and training-free compression techniques.

Key Contributions

Introduces 'Breadcrumbs Reasoning', a method for memory-efficient long-context reasoning in Transformers by periodically compressing the KV cache using learned 'compression beacons' and evicting entries. Trained via a joint distillation and RL framework, it achieves a superior memory-accuracy Pareto frontier compared to uncompressed models and training-free compression techniques.

Business Value

Enables LLMs to handle much longer contexts more efficiently, reducing operational costs and expanding their applicability to tasks requiring extensive background information, such as document analysis, long-form content generation, and complex Q&A.

Paper Metadata

Innovation Type

New Compression Technique and Training Method

Deployment Feasibility

Moderate. Requires modifications to the Transformer inference process and a specialized training procedure.

Limitations Addressed

Linear growth of Transformer KV cache,Significant memory and computational costs,Scalability constraints for long-context reasoning

Performance Gains

Superior memory-accuracy Pareto frontier

Technical Tags

long-context reasoningKV cache compressionmemory efficiencyTransformerdistillationreinforcement learning (RL)Pareto frontiercompression beaconsspecial-purpose tokens

Research Topics

LLM ScalabilityEfficient AILong-Context ProcessingMemory ManagementReinforcement Learning Applications

Methods & Architectures

KV cache compressionLearned compression tokens ('compression beacons')Joint distillation and RL trainingEviction of compressed entries TransformerLarge Language Models (LLMs)

Applications & Tasks

Natural Language Processing AI Efficiency Long-Context Tasks Scalability Constraints of LLMsHigh Memory/Computational Costs of KV CacheDiminishing Informational Value of Past Tokens Long-Context ReasoningMemory-Efficient LLM InferenceReducing KV Cache Overhead

Related Fields

Machine LearningNatural Language ProcessingDeep LearningReinforcement LearningComputer Architecture

Keywords

LLMlong contextKV cachecompressionmemory efficiencyTransformerreasoningRLdistillationscalability

Academic Context

#LLM Scalability#Efficient AI#Long-Context Processing#Memory Management#Reinforcement Learning Applications

Commercial Potential

Potential Products

Efficient LLM inference enginesLibraries for long-context processing

Target Industries

TechnologyCloud ComputingData AnalysisContent Creation

Use Case Examples

Summarizing entire books or long legal documentsAnswering questions based on extensive research papersGenerating coherent narratives over very long sequences

Competitive Edge

Offers a novel, learned approach to KV cache compression that directly optimizes for the memory-accuracy trade-off, outperforming existing training-free methods and enabling better long-context reasoning.

Market Opportunity

Growing demand for LLMs capable of handling longer contexts efficiently.

Revenue Models

Licensing of the compression technologyoffering optimized LLM services.

Resource Requirements

Compute Needs

High, for training the model with joint distillation and RL.

Data Requirements

Requires datasets suitable for long-context reasoning tasks.

Deployment Constraints

Integration into existing LLM inference pipelines, potential overhead from compression/eviction logic.

Scalability

The method is designed to improve scalability for long contexts.

Regulatory Considerations

None explicitly mentioned.

Production Readiness

Maturity Level

Research/Development

Time to Market

1-3 years for integration into production LLM frameworks.

Patent Potential

Moderate, for the compression beacon mechanism and training framework.

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