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s3: The new RAG framework that trains search agents with minimal data
S3 decouples RAG search from generation, boosting efficiency and generalization for enterprise LLM applications with minimal data.
The most recent phase, “RL-Zero,” leverages reinforcement learning(RL) to train models to act as search agents, improving through outcome-based feedback like answer correctness. This lets companies plug in any off-the-shelf or proprietary LLM—whether GPT-4, Claude, or an internal model—without having to fine-tune it,” Patrick (Pengcheng) Jiang, lead author of the paper and doctoral student at UIUC, told VentureBeat. “We see immediate potential in healthcare, enterprise knowledge management, and scientific research support, where high retrieval quality is critical and labeled data is often scarce,” Jiang said.
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