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[2602.20945] The Art of Efficient Reasoning: Data, Reward, and Optimization
Summary
This article explores efficient reasoning in Large Language Models (LLMs), focusing on optimizing computational resources through reward shaping and fine-tuned metrics.
Why It Matters
As LLMs become increasingly prevalent, optimizing their reasoning capabilities is crucial for enhancing performance while reducing computational costs. This research provides insights into effective training methodologies that can lead to more efficient AI systems, which is vital for both developers and researchers in the field.
Key Takeaways
- Efficient reasoning in LLMs can significantly reduce computational overhead.
- Reward shaping through Reinforcement Learning enhances reasoning accuracy.
- Training on easier prompts helps maintain a positive reward density, avoiding length collapse.
- Fine-grained metrics are essential for evaluating LLM performance across varying token budgets.
- The findings are validated across multiple models, demonstrating robustness and generalization.
Computer Science > Computation and Language arXiv:2602.20945 (cs) [Submitted on 24 Feb 2026] Title:The Art of Efficient Reasoning: Data, Reward, and Optimization Authors:Taiqiang Wu, Zenan Zu, Bo Zhou, Ngai Wong View a PDF of the paper titled The Art of Efficient Reasoning: Data, Reward, and Optimization, by Taiqiang Wu and 3 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) consistently benefit from scaled Chain-of-Thought (CoT) reasoning, but also suffer from heavy computational overhead. To address this issue, efficient reasoning aims to incentivize short yet accurate thinking trajectories, typically through reward shaping with Reinforcement Learning (RL). In this paper, we systematically investigate the mechanics of efficient reasoning for LLMs. For comprehensive evaluation, we advocate for more fine-grained metrics, including length distribution conditioned on correctness and performance across a wide spectrum of token budgets ranging from 2k to 32k. First, we reveal that the training process follows a two-stage paradigm: length adaptation and reasoning refinement. After that, we conduct extensive experiments (about 0.2 million GPU hours) in a unified protocol, deconstructing training prompts and rollouts, reward shaping, and optimization strategies. In particular, a key finding is to train on relatively easier prompts, ensuring the density of positive reward signals and thus avoiding the length collapse. Meanwhile, the learned length bi...
