Dec 23, 2025 · 12 min
Post-training and reinforcement learning emerge as the new AI frontier
An audio version of my blog post, Thoughts on AI progress (Dec 2025)
As traditional scaling methods encounter practical limits, the AI industry must pivot to new training paradigms to sustain rapid cognitive progress.
- 1Post-training techniques like reinforcement learning are becoming the primary drivers of model intelligence over raw data scaling.
- 2Achieving artificial general intelligence may require models to master multi-step planning and autonomous self-correction.
- 3The future of AI development is shifting toward agentic systems that operate like specialized, cooperative software.
The brief
The narrative surrounding artificial intelligence is shifting from raw scaling power to the complex engineering of post-training, where reinforcement learning and specialized fine-tuning are redefining the limits of large language models.
While early breakthroughs relied heavily on massive pre-training datasets, the next frontier of progress lies in teaching models how to reason, self-correct, and execute multi-step planning in dynamic environments.
This shift raises critical questions about whether current deep learning architectures can achieve artificial general intelligence, or if entirely new paradigms in computational neuroscience and system design are required.
Ultimately, the path to advanced AI may look less like a single massive brain and more like highly specialized software systems, where models act as cooperative agents solving targeted, highly complex tasks.
Reinforcement Learning
Artificial General Intelligence
Deep learningBooks & mentions
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