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

arXiv · Tingyun Li, Wenfeng Feng, Weiqing Li, Abudukelimu Wuerkaixi, Guohua Liu, Yuewei Zhang · 2026-09-05

This paper tackles a deceptively simple question: when should an LLM actually reuse its past experience during autonomous post-training, and when is that reuse actively harmful?

What it does

The paper introduces a conditional framework for experience transfer during autonomous LLM post-training, where the model decides dynamically whether to leverage prior task experience or start fresh. The authors develop a mechanism that evaluates the relevance and compatibility of past experiences before deciding to transfer knowledge, preventing negative transfer from outdated or conflicting training data. They demonstrate that unconditional experience reuse can degrade performance on new tasks, and their conditional approach significantly outperforms both full reuse and no-reuse baselines across multiple benchmarks.

Why it matters

This is directly actionable for anyone doing LLM post-training, fine-tuning, or building agentic systems that learn continuously. The core insight—that more experience isn't always better and that blind reuse of past knowledge can be actively detrimental—addresses a critical gap in how we think about continual learning for LLMs. As practitioners deploy models that must adapt to new domains without forgetting old ones, this conditional transfer framework provides a concrete mechanism to manage that trade-off.

How it applies

Practitioners building autonomous agents that learn from interaction could implement similar conditional gates to decide when to update their knowledge base versus when to treat new situations as genuinely novel. This applies directly to RLHF pipelines, continual fine-tuning workflows, and any system where an LLM accumulates experience over time. The framework could also inform how we design memory systems in agentic architectures—knowing what to remember, what to forget, and what to treat as context-dependent.

Takeaways

• Unconditional experience reuse in LLM post-training can cause negative transfer, degrading performance on new tasks.
• A conditional gating mechanism that evaluates experience relevance before transfer significantly outperforms both full reuse and starting from scratch.
• The framework provides a practical tool for managing catastrophic forgetting in continually learning LLM systems.
• Not all past experience is equally valuable—the key is learning when to trust prior knowledge and when to treat new data as genuinely novel.

Topics: LLM-post-training, continual-learning, experience-replay, negative-transfer, agentic-systems