Chain-of-Draft

What is Chain-of-Draft?

Chain-of-Draft (CoD) is an LLM optimization for multi-step reasoning algorithms. The aim of the method is to reduce the number of tokens produced, and therefore processed, by the AI engine as it performs interim reasoning steps. Other algorithms such as Chain-of-Thought can be expensive because the model generates progressively better results as it steps through a reasoning process. Chain-of-Draft is a type of CoT token reduction optimization, specifically focused on LLM reasoning models.

Research on Chain-of-Draft

Research papers include:

• Silei Xu, Wenhao Xie, Lingxiao Zhao, Pengcheng He, 25 Feb 2025, Chain of Draft: Thinking Faster by Writing Less, https://arxiv.org/abs/2502.18600 (Concise CoT method using a per-step inference budget.)
• Dr. Ashish Bamania, March 3rd, 2025, Chain-of-Draft (CoD) Is The New King Of Prompting Techniques: A deep dive into the novel Chain-of-Draft (CoD) Prompting that outperforms Chain-of-Thought (CoT) Prompting while reducing LLM inference cost and latency like never before, https://levelup.gitconnected.com/chain-of-draft-cod-is-the-new-king-of-prompting-techniques-d9dc17f12051
• Michael Nuñez, March 3, 2025, Less is more: How ‘chain of draft’ could cut AI costs by 90% while improving performance, https://venturebeat.com/ai/less-is-more-how-chain-of-draft-could-cut-ai-costs-by-90-while-improving-performance/
• Reddit, March 01, 2025, Chain of Draft: Streamlining LLM Reasoning with Minimal Token Generation, https://www.reddit.com/r/artificial/comments/1j04ezf/chain_of_draft_streamlining_llm_reasoning_with/
• Sulbha Jain, March 02, 2025, Chain of Draft: Thinking Faster by Writing Less — Paper Review, https://medium.com/@sulbha.jindal/chain-of-draft-thinking-faster-by-writing-less-paper-review-20e57bfc867a
• Ajith Vallath Prabhakar, March 2, 2025, Chain of Draft: The Breakthrough Prompting Technique That Makes LLMs Think Faster With Less, https://ajithp.com/2025/03/02/chain-of-draft-llm-prompting/
• The Decoder, Mar 2, 2025, Chain of Draft Prompts lets LLMs think cheaper with fewer words, https://the-decoder.com/chain-of-draft-prompts-lets-llms-think-cheaper-with-fewer-words/
• Sebastian Raschka, Mar 8, 2025, Inference-Time Compute Scaling Methods to Improve Reasoning Models: Part 1: Inference-Time Compute Scaling Methods, https://sebastianraschka.com/blog/2025/state-of-llm-reasoning-and-inference-scaling.html

Reasoning and CoT Efficiency Topics

Blog articles on reasoning efficiency:

• Reasoning Inference Optimization
• Reasoning Decoding

More research information on general efficiency optimization techniques for reasoning models:

• Reasoning inference optimization (RIO)
• Chain-of-Thought (CoT) optimization
• Small Reasoning Models (SRMs)
• Adaptive Inference Time Compute

Efficiency optimizations to Chain-of-Thought include:

• Hidden Token Chain-of-Thought (HCot)
• Continuous Chain-of-Thought (Coconut)
• CoT Reasoning Decoding
• Concise Chain-of-Thought
• CoT Token Reduction
• CoT Step Skipping
• CoT Early Stopping
• CoT Path Reduction
• Constrained Chain-of-Thought

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