BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data
arXiv:2605.25549v1 Announce Type: new Abstract: High-quality expert chain-of-thought (CoT) data is one of the core bottlenecks in large language model (LLM) post-training. Existing data production methods each have structural limitations: crowdsourced annotation lacks deep reasoning paths; expert solo writing is constrained by the "expert blind spot" -- experts structurally skip reasoning steps they consider obvious; RLHF only produces preference signals rather than reasoning chains. This paper proposes the BC Protocol -- a structured dual-expert elicitation method for LLM post-training data p
The rapid advancement of large language models is increasingly bottlenecked by high-quality post-training data, particularly complex reasoning chains, making new data elicitation methods critically timely.
Improving post-training data quality is crucial for enhancing LLM capabilities, directly impacting the performance and reliability of advanced AI systems and their commercial applications.
The proposed BC Protocol introduces a structured methodology for generating higher quality expert chain-of-thought data, potentially accelerating LLM development and reducing dependence on less effective current methods.
- · AI model developers
- · Data annotation companies specializing in expert systems
- · Researchers in AI safety and alignment
- · Crowdsourced annotation platforms
- · LLM training methods reliant solely on solo expert writing
Higher quality expert chain-of-thought data becomes more accessible for training advanced AI models.
LLMs exhibit improved reasoning, accuracy, and reduced 'hallucinations' in complex tasks, leading to more robust AI agents.
The enhanced capabilities of LLMs could accelerate the development and deployment of autonomous AI agents across various industries, collapsing white-collar workflows.
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