arXiv:2602.02979v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated strong potential in complex reasoning, yet their progress remains fundamentally constrained by reliance on massive high-quality human-curated tasks and labels, either through supervised fine-tuning (SFT) or reinforcement learning (RL) on reasoning-specific data. This dependence renders supervision-heavy training paradigms increasingly unsustainable, with signs of diminishing scalability already evident in practice. To overcome this limitation, we introduce CPM\"obius (CPMobius), a collaborative C
The increasing cost and diminishing returns of traditional large language model training methods are pushing research towards more efficient and autonomous learning paradigms.
This development addresses a fundamental constraint in AI, potentially enabling more scalable and resource-independent development of complex reasoning capabilities.
The reliance on massive human-curated datasets for LLM training might decrease, shifting focus towards self-improving and data-free learning architectures.
- · AI research labs focused on independent learning
- · Developers with limited access to vast curated datasets
- · Organizations seeking more efficient AI development
- · Data labeling companies focused on reasoning tasks
- · LLM developers solely reliant on SFT/RL with labeled data
More sophisticated and self-sufficient AI systems can be developed with fewer human resources.
This could accelerate the deployment of advanced AI agents in various sectors without proportional increases in data annotation budgets.
Reduced data dependency might democratize access to advanced AI development, fostering more diverse innovation outside of established tech giants.
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Read at arXiv cs.CL