Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training

Large language models offer broad capabilities, but adapting them to evolving domains, tools, and requirements often entails repeated post-training. Autonomous systems automate parts of this process by proposing updates, training candidates, and using evaluation feedback to select subsequent proposals. As evidence accumulates, a central problem emerges: which past update evidence remains actionable after subsequent training has changed the parent model? An update’s effect depends on its parent, data, and training stage. Treating past success as context-free permission can waste compute. If the resulting child is promoted, it can also degrade the subsequent training trajectory. We formulate this problem as conditional experience transfer and introduce Boundary-Calibrated Intervention Transfer (BCIT), a method that authorizes experience reuse before weight-changing training. BCIT binds an observed effect to its source context, checks applicability conditions, vetoes candidates with named hard conflicts, and obtains current-state evidence through a bounded training trial when needed. Fully trained candidates still face a shared adoption rule, and only observed events extend memory.

Introduction. Large language models can solve a broad range of tasks, but adapting them to new domains, tools, and requirements often demands repeated post-training. Autonomous systems automate parts of this loop: they propose an update, train a candidate, evaluate it, and use the feedback to select or revise subsequent proposals (Yano, Ishibashi, and Oyamada 2025; Rank et al. 2026; Ma et al. 2026; Chen et al. 2026a,b). The loop also accumulates a potentially valuable history of updates and outcomes. That history is not self-executing. An update’s effect depends on its parent model, data mixture, training stage, and evaluation contract. Evidence that an update was beneficial under one source context may therefore be misleading under the current context. Incorrectly authorizing a contextincompatible update consumes scarce training budget; if the trained child is then promoted, it also changes the parent checkpoint and the relevance of later evidence.

Discussion / Conclusion. Past success in one source context does not authorize modifying every future parent. BCIT enforces this distinction: it rejects named hard conflicts, validates unresolved candidates on the current parent, and grants full-training budget only under a frozen rule. A policy-shared adoption rule then promotes or rolls back each trained child. Across the evaluated evidence chain, update effects vary across contexts. Under matched information, BCIT authorizes fewer harmful candidates while retaining beneficial ones. Under matched compute, it attains a higher cross-task mean than all evaluated shared-model alternatives. These results support evidence-conditioned rather than unconditional allocation of post-training compute.