LLM training

Most people still think LLM progress comes from bigger pre-training runs.
That’s no longer true.

Today, the real gains happen after pre-training—in alignment and control.

Modern LLMs follow a two-phase pipeline:
pre-training creates capability; post-training creates usable systems.

Supervised fine-tuning teaches format and instructions, but it can’t capture human preference.
That’s why reinforcement learning matters.

PPO → GRPO → DPO isn’t just an algorithm shift.
It’s a move from research-heavy pipelines to engineering-efficient stacks.

Reward models, critics, and complex loops are being simplified or removed.
Alignment is becoming cheaper, faster, and operational.

As model intelligence commoditizes, control becomes the moat.

The next AI winners won’t train the biggest models—
they’ll ship the most reliable, aligned ones at scale.