Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and proteins. These models are built through post-training, yet how each stage shapes reasoning and generalization remains poorly understood. We study when post-training improves performance and when it induces over-specialization. Across genomics, transcriptomics, and proteins, we train and evaluate more than 100 biological reasoning models under controlled variation in backbone, continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL), measuring both in-domain (ID) and out-of-domain (OOD) performance. We find that each post-training stage reshapes generalization in a distinct way rather than contributing uniform gains. CPT improves downstream performance by aligning models with biological language. SFT consistently increases ID performance but causes OOD performance to peak early and decline as models fit the training distribution. RL, when applied to strong SFT checkpoints with aligned rewards, improves OOD performance and partially recovers generalization. These results show that biological reasoning does not improve monotonically with additional supervision or compute. Instead, performance depends on how training stages are composed. Under fixed post-training budgets, the strongest ID-OOD trade-off comes from brief SFT, larger RL allocations, and asymmetric adaptation capacity across stages.

翻译：用于生物学的科学推理模型将语言模型与基于多模态生物数据（包括DNA、RNA和蛋白质）训练的基座模型相结合。这些模型通过后训练构建，然而每个阶段如何影响推理与泛化能力仍未被充分理解。我们研究了后训练何时提升性能，以及何时导致过度专化。在基因组学、转录组学和蛋白质领域，我们通过可控变化（包括骨干网络、持续预训练、监督微调和强化学习）训练并评估了超过100个生物推理模型，同时测量了领域内和领域外性能。我们发现每个后训练阶段以不同方式重塑泛化能力，而非贡献一致的收益。持续预训练通过使模型与生物语言对齐来提升下游性能。监督微调持续提高领域内性能，但导致领域外性能过早达到峰值并随模型拟合训练分布而下降。当将强化学习应用于具有对齐奖励的强监督微调检查点时，它能提升领域外性能并部分恢复泛化能力。这些结果表明生物推理并非随额外监督或计算量单调提升。相反，性能取决于训练阶段的组合方式。在固定后训练预算下，最强的领域内-领域外权衡来自较短的监督微调、更大的强化学习分配以及各阶段间非对称的适配能力。