CLORE: Content-Level Optimization for Reasoning Efficiency

2026-05-23 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Large Language Models · Depth: Expert, extended

Summary

CLORE (Content-Level Optimization for Reasoning Efficiency) is a new framework designed to improve the accuracy-efficiency trade-off in large language models by optimizing intermediate reasoning content. Unlike existing methods that primarily control response length, CLORE uses an external augmentation model, such as Qwen3-4B-Instruct-2507, to edit correct on-policy rollouts. It deletes repetitive segments, illegible or task-irrelevant content, and superfluous reasoning after the solution is found, while preserving the final answer. The framework then optimizes augmented-original pairs using an auxiliary reference-free DPO objective alongside standard policy-gradient training. Experiments on DeepSeek-R1-Distill-Qwen-7B and Qwen2.5-Math-7B across five mathematical reasoning benchmarks (OlympiadBench, Minerva, MATH500, AMC2023, AIME2025) show CLORE improves Accuracy-Efficiency (AE) scores and reduces output length by 20-50% without significant accuracy loss.

Key takeaway

For AI Scientists and Machine Learning Engineers focused on deploying efficient LLMs for mathematical reasoning, CLORE offers a critical approach to enhance model performance beyond mere length control. You should consider integrating content-level optimization to reduce repetitive, illegible, or post-answer exploration in reasoning traces. This method, compatible with existing length-based techniques like GRPO or DAPO, can yield substantial output length reductions (20-50%) while maintaining or improving accuracy, leading to better accuracy-efficiency trade-offs and lower inference costs.

Key insights

CLORE optimizes LLM reasoning efficiency by content-level editing of correct trajectories, reducing redundancy and illegibility.

Principles

• Content-level supervision complements length-based control.
• Augmenting only correct trajectories mitigates off-policy mismatch.
• A moderate DPO weight balances accuracy and length trade-offs.

Method

CLORE samples on-policy trajectories, augments correct ones by deleting low-quality content via an external LLM, then optimizes augmented-original pairs with a reference-free DPO objective alongside policy-gradient training.

In practice

• Use an external LLM for deletion-based content editing.
• Filter augmented trajectories for correctness consistency.
• Combine DPO with policy-gradient for stable training.

Topics

• LLM Reasoning Efficiency
• Content-Level Optimization
• Reinforcement Learning
• Direct Preference Optimization
• Mathematical Reasoning Benchmarks
• DeepSeek-R1-Distill-Qwen-7B
• Qwen2.5-Math-7B

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• CLORE: Content-Level Optimization for Reasoning Efficiency — CLORE optimizes LLM reasoning efficiency by editing content within correct trajectories, reducing repetition and irrelevance while preserving answers.
• Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization — LCPO efficiently prunes lengthy LRM Chain-of-Thought outputs by balancing NLL loss with small-scale preference optimization.
• Finding the Optimal Reasoning Budget for LLMs — Optimal LLM reasoning length varies by model, while context compression and reasoning failures are critical research areas.
• You Only Index Once: Cross-Layer Sparse Attention with Shared Routing — Cross-layer sparse attention shares a single routing index across decoder layers to amortize top-k cost and boost LLM inference efficiency.
• Thinking with Reasoning Skills: Fewer Tokens, More Accuracy — Distilling and retrieving reusable reasoning skills significantly improves LLM efficiency and performance.
• Paper: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning — DeepSeek R1 enhances LLM reasoning via pure reinforcement learning and rule-based rewards, fostering autonomous problem-solving without supervised data.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.