Scaling LLM Reasoning from Minimal Labels: A Semi-Supervised Framework with a Lightweight Verifier
arXiv:2606.16811v1 Announce Type: cross Abstract: For the development of Large language models (LLMs), recent approaches to generating pseudo intermediate reasoning have shown remarkable progress. But they typically rely on large numbers of correctly annotated answers to assess reasoning quality. This paper presents a semi-supervised framework that scales reasoning learning from minimal supervision, turning reasoning verification itself into a data creation mechanism. We train a lightweight reasoning-correctness classifier on only a few labeled samples, which judges whether intermediate reason
This paper addresses a current bottleneck in LLM development, the reliance on extensive human annotation for reasoning assessment, which is becoming increasingly expensive and time-consuming.
This development could significantly accelerate the training and deployment of more capable LLMs by reducing the need for costly human labeling, making advanced AI development more accessible and efficient.
The paradigm shifts from needing large, perfectly labeled datasets for reasoning verification to a semi-supervised approach leveraging lightweight verifiers and minimal human input.
- · AI developers and research labs
- · Companies seeking to deploy advanced LLMs
- · Sectors reliant on complex automated reasoning
- · Manual data annotation services (in the long term)
- · LLM development approaches heavily reliant on fully supervised learning
Faster and cheaper development cycles for sophisticated AI systems capable of complex reasoning.
Increased competition and accessibility in advanced AI model creation, potentially democratizing access to powerful AI.
Acceleration of AI agent development as reasoning capabilities become more robust and scalable, impacting white-collar workflows faster than anticipated.
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Read at arXiv cs.CL