In this episode of the Neural Intel deep dive, we go under the hood of a groundbreaking study on Iterative Deployment. While many fear "model collapse" from training on synthetic data, researchers have found that an explicit curation step—filtering for only valid, high-quality traces—can actually trigger emergent generalization.We discuss the formal proof that iterative deployment is a special case of the REINFORCE algorithm, where the reward signal is left implicit rather than explicitly defined,. This "outer-loop" training mirrors how models like GPT-3.5 and GPT-4 were developed using web-scraped data from their predecessors. We also tackle the critical AI safety concerns: if the reward function is opaque and driven by user interactions, how do we prevent it from clashing with safety alignments,?Join us as we analyze results from classical planning domains like Blocksworld and Sokoban, where later generations found significantly longer and more efficient plans than their base models.

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