Autodata introduces an agentic method with meta-optimization to create higher-quality synthetic data, yielding performance gains over standard methods on CS, legal, and math tasks.
arXiv preprint arXiv:2512.23707 , year=
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Rubric-based RL verifiers can be gamed via partial criterion satisfaction and implicit-to-explicit tricks, yielding proxy gains that do not improve quality under rubric-free judges; stronger verifiers reduce but do not eliminate the mismatch.
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.
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Autodata: An agentic data scientist to create high quality synthetic data
Autodata introduces an agentic method with meta-optimization to create higher-quality synthetic data, yielding performance gains over standard methods on CS, legal, and math tasks.
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Reward Hacking in Rubric-Based Reinforcement Learning
Rubric-based RL verifiers can be gamed via partial criterion satisfaction and implicit-to-explicit tricks, yielding proxy gains that do not improve quality under rubric-free judges; stronger verifiers reduce but do not eliminate the mismatch.
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AI for Auto-Research: Roadmap & User Guide
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.