Assess robustness of results under advanced post-training methods

Determine whether the alignment pretraining effects reported for supervised fine-tuning and direct preference optimization persist, diminish, or change when applying reinforcement learning with verifiable rewards, reasoning-focused post-training, deliberative alignment, or constitutional AI.

Background

The study uses a minimalist open-source post-training pipeline (SFT + DPO) following Olmo 3 to allocate most compute to pretraining interventions. Frontier labs commonly employ additional post-training techniques such as RL with verifiable rewards, deliberate reasoning-focused procedures, and constitutional AI.

The authors explicitly state uncertainty about whether using these stronger or different post-training methods would significantly alter their findings on the persistence and benefits of alignment pretraining.

References

It is unclear whether our findings would be significantly affected by the implementations of these techniques.

— Alignment Pretraining: AI Discourse Causes Self-Fulfilling (Mis)alignment  (2601.10160 - Tice et al., 15 Jan 2026) in Section 6, Limitations – Simplistic Post-Training

Taken together, these results suggest that, in our setup, knowledge-aligned data construction can achieve gains comparable to additional post-training stages; whether these gains stack with factuality-oriented RL remains open.

— Stick to What You Know: A Study of Knowledge-Aligned Supervised Fine-Tuning  (2608.30987 - Becker et al., 31 Aug 2026) in Section 4.2, Multi-Stage Post-Training

Two clear open problems remain: determining whether persona binding survives RL and, if not, designing RL recipes that preserve or actively reinforce it.

— Synthetic Persona Pretraining: Alignment from Token Zero  (2608.13482 - Minder et al., 13 Aug 2026) in Discussion and Conclusion, paragraph “How can we install a stable persona?”