Mid-training and Post-training Continue to Heat Up, Models Iterate Rapidly, Becoming the Best Testing Ground for AI for AI
Leiphone.com explores the "AI for AI" trend: large models are gradually participating in the research and development of next-generation models, automating literature reading, solution generation, code writing, compute scheduling, and experimental verification, becoming the core proposition of model iteration.
Key Takeaways
- Key Highlight：Leiphone.com explores the "AI for AI" trend: large models are gradually participating in the research and development of next-generation models, automating literature reading, solution generation, code writing, compute scheduling, and experimental verification, becoming the core proposition of model iteration.
- Innovation & Tech：Highlights advancements in Mid-training, Post-training, Continue, demonstrating rapid progress in model capabilities.
- Industry Impact：Reported via 雷峰网 (CN), offering actionable signals for developers and technology leaders.
"AI for AI" refers to using artificial intelligence to assist in or even lead the research and development process of next-generation AI models. Traditional large model development relies heavily on manual labor: researchers need to write training scripts, adjust parameters, queue and wait for GPU cluster scheduling, and many innovative ideas are shelved due to engineering burdens. When the model itself can read cutting-edge literature, generate candidate solutions, automatically write code, and schedule computing power to run ablation experiments in parallel, the R&D paradigm is being redefined.
This trend is important because it directly addresses the bottleneck problem of large model R&D efficiency. New architectural hypotheses and training strategies emerge one after another, but the cost of verification is extremely high, and many ideas are abandoned before being fully tested. If AI can take on a large amount of repetitive and mechanical experimental execution work, researchers can focus on higher-level theoretical innovation and direction judgment, thereby accelerating the pace of model iteration.
From a technical perspective, "AI for AI" is not a simple tool upgrade, but involves a deep restructuring of the mid-training and post-training stages. Based on understanding research intent, models need to autonomously complete solution screening, code generation, resource scheduling, and result analysis, which places higher demands on the model's own reasoning ability, tool invocation ability, and long-context processing ability.
This direction may also bring about a chain reaction at the industry level. The improvement of computing power scheduling efficiency means the optimization of GPU cluster utilization, indirectly alleviating the pressure on computing power supply; at the same time, a model R&D system with "self-iteration" capabilities may become a competitive barrier for top laboratories, further widening the technological gap between different institutions. However, this model is currently still in the exploratory stage, and its actual effectiveness and safety need to be continuously verified.
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Industry Insights & Analysis
As artificial intelligence rapidly evolves, breakthroughs surrounding Mid-training, Post-training, Continue, Heat are shifting toward scalable, robust real-world implementations.
Driven by both open-source ecosystems and proprietary model architectures, the integration between compute optimization, data engineering, and agentic workflows is accelerating. This development provides a strategic benchmark for upcoming AI tooling and developer workflows.