"Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability"

Fix one token, fix the whole math solution - that's what this paper discovered.

This paper shows how single tokens can make or break LLM's reasoning ability.

📌 Paper: "Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability"

This paper introduces a method to improve LLMs' reasoning by identifying and fixing critical tokens that cause incorrect solutions. The approach uses contrastive estimation to detect problematic tokens and incorporates token-level rewards during model alignment, significantly boosting mathematical reasoning performance.

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https://arxiv.org/abs/2411.19943

🤔 Original Problem:

LLMs struggle with reasoning tasks despite using alignment techniques like Direct Preference Optimization (DPO). Current methods focus on example-level optimization but miss the impact of individual tokens on reasoning outcomes.

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🔧 Solution in this Paper:

→ The paper introduces cDPO, which automatically identifies critical tokens in incorrect reasoning paths using contrastive estimation.

→ It trains separate models on correct and incorrect reasoning examples to learn pattern differences.

→ The method compares token generation likelihoods between these models to spot problematic tokens.

→ cDPO extends DPO to token-level optimization, using the identified critical tokens as weighted rewards during training.

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💡 Key Insights:

→ Small changes in operators and logical elements can drastically affect reasoning outcomes

→ Forcing models to avoid critical tokens significantly improves solution accuracy

→ Token-level optimization outperforms traditional example-level approaches

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📊 Results:

→ Achieved 90.8% accuracy on GSM8K with Llama-3-70B

→ Improved MATH500 performance by 3.3% over baseline methods

→ Statistical significance with p<0.005 across all benchmarks