ZeroSearch uses supervised fine-tuning to create a simulated retrieval module and curriculum-based RL rollouts that degrade document quality to train LLMs on search capabilities without real search API calls.
arXiv preprint arXiv:2309.13078 , year =
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POTracker fine-tunes an LLM with POTrackerLoss combining textual and structural similarity, achieving up to 86.47% structural accuracy on 1,000 power outage reports and outperforming baselines by up to 51%.
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ZeroSearch: Incentivize the Search Capability of LLMs without Searching
ZeroSearch uses supervised fine-tuning to create a simulated retrieval module and curriculum-based RL rollouts that degrade document quality to train LLMs on search capabilities without real search API calls.
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POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation
POTracker fine-tunes an LLM with POTrackerLoss combining textual and structural similarity, achieving up to 86.47% structural accuracy on 1,000 power outage reports and outperforming baselines by up to 51%.