"Token-Budget-Aware LLM Reasoning"

Smart token budgeting helps LLMs reason better while using fewer resources.

TALE (Token-Budget-Aware LLM rEasoning) framework dynamically estimates and applies token budgets for Chain-of-Thought reasoning in LLMs, reducing token usage while maintaining accuracy.

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https://arxiv.org/abs/2412.18547

🤔 Original Problem:

→ Chain-of-Thought (CoT) reasoning in LLMs creates significant token overhead and increased costs, making it expensive for real-world applications.

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🔧 Solution in this Paper:

→ TALE (Token-Budget-Aware LLM rEasoning) estimates appropriate token budgets based on reasoning complexity.

→ It uses a zero-shot estimator where the LLM itself predicts required tokens for a given task.

→ The framework implements a greedy search strategy to find optimal budgets that balance token efficiency with answer accuracy.

→ TALE incorporates token budget awareness through fine-tuning, helping LLMs internalize efficient reasoning patterns.

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💡 Key Insights:

→ LLM reasoning can be significantly compressed with proper token budgets

→ Token elasticity exists - when budgets are too small, actual token usage paradoxically increases

→ Budget estimation achieves 60.61% in-range accuracy for predicting optimal token requirements

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📊 Results:

→ Reduces token usage by 68.64% while maintaining accuracy within 5% of original performance

→ Achieves 84.46% accuracy on GSM8K dataset, surpassing vanilla CoT

→ Demonstrates consistent performance across Yi-lightning, GPT-4o-mini, and GPT-4o models

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