Post-training LLMs feel like one-function, one-reward work that scales up

ivan_bezdomny · x · 2026-08-04

The author says they love working on post-training LLMs because the work feels unusually focused and transferable:

• a lot of effort collapses into one function, one reward, one number
• what works on 1B-parameter models often transfers to much larger ones
• you can’t just automatically RL your way to a better reward function

It’s a concise reflection on why post-training is appealing: the leverage is high, but the core bottleneck is still reward design rather than endlessly better RL algorithms.

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