Same Targets, Different Computation: How Post-Training Divides Work Across Model Layers
A late-layer change learned during post-training may work on the base model's earlier state, or it may depend on earlier computation learned with it. We distinguish these cases with a four-cell diagnostic that crosses base or descendant upstream states with base or descendant late stacks. A large late-stack effect need not imply strong upstream dependence. On math prompts, OpenMath2's late stack changes the target margin by +3.43 logits after base upstream state and +3.28 after its own, giving a near-zero interaction. Several instruction-following descendants of the same Llama-3.1-8B base show greater dependence, while controlled code and biomedical continuation-training runs sit near zer...
| Authors | Yifan Zhou |
|---|---|
| Primary category | cs.LG |
| Published | 8 May 2026 |
| Last updated at source | 10 August 2026 |
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