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  "text": "Small models are cheap to run, but expensive to adapt.\n\nThe hard part is not only fine-tuning. It is the surrounding loop that involves collecting data, diagnosing failures, building evals, avoiding regressions, choosing curricula, and deciding when an update is safe.\n\nThis new paper introduces Pioneer Agent, a closed-loop system for continual improvement of small language models in production.\n\nIn cold-start mode, the agent starts from a natural-language task description, acquires data, builds evals, and iteratively trains models. In production mode, it uses labeled failures to diagnose error patterns, synthesize targeted data, and retrain under explicit regression constraints.\n\nThe results are strong: gains of 1.6 to 83.8 points across eight cold-start benchmarks, no regressions across seven AdaptFT-Bench scenarios, intent classification from 84.9% to 99.3%, and Entity F1 from 0.345 to 0.810.\n\nPaper: https://t.co/lFkFiXzP8E\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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