CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection
CoT-Core is a training-free core question selection framework that accelerates LLM evaluation by leveraging zero-shot Chain-of-Thought reasoning trajectories It addresses two key limitations of existing coreset methods: the "cold start" bottleneck requiring massive historical logs and surface lexical bias that misses underlying reasoning structures The method projects CoT reasoning paths into a latent space to cluster questions by intrinsic logical equivalence rather than superficial text simila
Analysis
TL;DR
- CoT-Core is a training-free core question selection framework that accelerates LLM evaluation by leveraging zero-shot Chain-of-Thought reasoning trajectories
- It addresses two key limitations of existing coreset methods: the "cold start" bottleneck requiring massive historical logs and surface lexical bias that misses underlying reasoning structures
- The method projects CoT reasoning paths into a latent space to cluster questions by intrinsic logical equivalence rather than superficial text similarity
- Experiments on GSM8K, MMLU, MMLU-Pro, and GPQA show CoT-Core drastically reduces evaluation costs while maintaining high-fidelity score estimation
- The framework's efficacy is intrinsically gated by task complexity, delineating clear boundary conditions for reasoning-aware pruning
Why It Matters
This work directly addresses one of the most pressing operational challenges in LLM development: the prohibitive computational cost of continuous evaluation. By eliminating the need for historical logs and moving beyond lexical similarity, CoT-Core offers a practical, training-free solution that can significantly reduce evaluation overhead without sacrificing accuracy. For AI practitioners running frequent benchmark cycles, this could translate to substantial cost savings and faster iteration timelines.
Technical Details
- Core Innovation: CoT-Core prompts LLMs to generate zero-shot Chain-of-Thought reasoning trajectories for each question, then projects these reasoning paths into a latent space where questions are clustered based on intrinsic logical equivalence rather than surface-level text similarity
- Problem Addressed: Existing coreset selection methods like Item Response Theory suffer from a "cold start" bottleneck requiring massive historical interaction logs, while other approaches exhibit lexical bias that fails to capture the underlying reasoning manifold of tasks
- Training-Free Design: The framework requires no additional training or fine-tuning, operating purely through prompting and latent space projection of existing model reasoning
- Benchmarks: Evaluated across four major benchmarks—GSM8K (mathematical reasoning), MMLU (general knowledge), MMLU-Pro (enhanced multiple-choice), and GPQA (graduate-level science)—demonstrating consistent cost reduction with high-fidelity score estimation
- Key Finding: The effectiveness of reasoning-aware pruning is intrinsically gated by task complexity, establishing clear boundary conditions for when this approach is most beneficial
Industry Insight
- Organizations running continuous LLM evaluation pipelines should consider adopting reasoning-aware coreset selection to reduce computational costs, particularly for high-frequency benchmarking scenarios where traditional methods become prohibitively expensive
- The finding that efficacy is gated by task complexity suggests a strategic tiered evaluation approach: apply CoT-Core aggressively for complex reasoning tasks where lexical bias is most damaging, while simpler tasks may still benefit from lighter-weight selection methods
- As LLM development cycles accelerate, training-free evaluation acceleration methods will become increasingly valuable; this work positions reasoning structure as a first-class signal for evaluation efficiency rather than an afterthought
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