arxiv_cl 95% Match Research Paper AI Researchers,ML Engineers,Developers working with LLMs 20 hours ago

TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers' Guidance

large-language-models › reasoning

📄 Abstract

Abstract: Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes. However, this enhanced reasoning capability results in an increased number of output tokens during inference, leading to higher computational costs. To address this challenge, we propose TwT (Thinking without Tokens), a method that reduces inference-time costs through habitual reasoning distillation with multi-teachers' guidance, while maintaining high performance. Our approach introduces a Habitual Reasoning Distillation method, which internalizes explicit reasoning into the model's habitual behavior through a Teacher-Guided compression strategy inspired by human cognition. Additionally, we propose Dual-Criteria Rejection Sampling (DCRS), a technique that generates a high-quality and diverse distillation dataset using multiple teacher models, making our method suitable for unsupervised scenarios. Experimental results demonstrate that TwT effectively reduces inference costs while preserving superior performance, achieving up to a 13.6% improvement in accuracy with fewer output tokens compared to other distillation methods, offering a highly practical solution for efficient LLM deployment.

Key Contributions

TwT introduces 'Habitual Reasoning Distillation' to internalize explicit reasoning into LLM behavior, inspired by human cognition, and uses 'Dual-Criteria Rejection Sampling' with multi-teacher guidance to create distillation datasets. This method significantly reduces inference costs and token usage while preserving high performance, addressing a key bottleneck in LLM deployment.

Business Value

Enables the deployment of powerful LLMs in resource-constrained environments or for applications requiring low latency and cost, such as real-time conversational agents or on-device AI.

Paper Metadata

Innovation Type

Algorithmic innovation

Deployment Feasibility

High, as it directly targets inference efficiency, a critical factor for deployment.

Limitations Addressed

High computational costs and increased token output during LLM inference, which hinders practical deployment.

Performance Gains

Significant reduction in inference costs and token consumption while maintaining high performance.

Technical Tags

LLM inferencecomputational cost reductionreasoning distillationhabitual reasoningmulti-teacher guidanceknowledge distillationdual-criteria rejection samplingunsupervised learning

Research Topics

Large Language ModelsEfficient AIMachine Learning OptimizationKnowledge DistillationReasoning in AI

Methods & Architectures

Habitual Reasoning DistillationTeacher-Guided CompressionDual-Criteria Rejection Sampling (DCRS)Multi-teacher distillation Large Language Models (LLMs)

Applications & Tasks

Natural Language Processing AI Inference Optimization Reducing LLM inference costImproving LLM efficiencyMaintaining performance with reduced tokens Efficient reasoning in LLMsCompressing reasoning processesDistilling complex reasoning

Related Fields

Machine LearningNatural Language ProcessingArtificial IntelligenceDeep Learning

Keywords

LLMinferenceefficiencyreasoningdistillationhabitualmulti-teachercost reductiontokenscomputational costDCRSunsupervised

Academic Context

#Large Language Models#Efficient AI#Machine Learning Optimization#Knowledge Distillation#Reasoning in AI

Commercial Potential

Potential Products

Optimized LLM inference enginesCost-effective LLM APIs

Target Industries

TechnologySaaSCustomer ServiceContent Creation

Use Case Examples

Deploying a chatbot with reduced latency and operational costs.Enabling complex reasoning tasks on edge devices.

Competitive Edge

TwT offers a novel approach to reasoning distillation that is inspired by human cognition, potentially leading to more effective and efficient internalized reasoning compared to standard distillation techniques.

Market Opportunity

The market for efficient LLM deployment is rapidly growing.

Revenue Models

Licensing of optimized modelsAPI access to efficient inference services.

Resource Requirements

Compute Needs

Moderate to high for training/distillation, but significantly reduced for inference.

Data Requirements

Requires datasets suitable for reasoning tasks and potentially multiple teacher models for distillation.

Deployment Constraints

Requires careful tuning of distillation process.

Scalability

The method is designed to improve scalability by reducing inference costs.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for optimized inference libraries

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