ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation
Iterative self-distillation enables LLM agents to learn from successive deployments, offering a path toward recursive self-improvement (RSI). Yet our experiments with existing methods reveal a collapse in deployment performance across cycles, while task performance with privileged information (PI) also declines. We address this collapse by prioritizing informative interaction s
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Iterative self-distillation enables LLM agents to learn from successive deployments, offering a path toward recursive self-improvement (RSI). Yet our experiments with existing methods reveal a collapse in deployment performance across cycles, while task performance with privileged information (PI) also declines. We address this collapse by prioritizing informative interaction s
This research addresses a critical challenge in iterative self-distillation for LLM agents, preventing performance collapse over successive learning cycles. It offers a method to ensure agents can continuously improve without degradation, which is vital for developing robust and reliable AI systems.