AutoPyVerifier learns compact sets of executable Python verifiers from labeled LLM outputs via LLM synthesis and DAG search, improving objective prediction by up to 55 F1 points and downstream LLM accuracy by up to 17 points.
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GroupDPO decouples group-wise preference optimization during backpropagation to cut peak memory while keeping the same gradients, allowing larger groups and consistent gains over single-pair DPO plus an NLL term on positives.
OASES co-trains a search policy and an outcome-aligned state evaluator so intermediate search steps get denser, outcome-consistent process rewards and beat strong RL baselines on multi-hop QA.
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AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
AutoPyVerifier learns compact sets of executable Python verifiers from labeled LLM outputs via LLM synthesis and DAG search, improving objective prediction by up to 55 F1 points and downstream LLM accuracy by up to 17 points.
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GroupDPO: Memory efficient Group-wise Direct Preference Optimization
GroupDPO decouples group-wise preference optimization during backpropagation to cut peak memory while keeping the same gradients, allowing larger groups and consistent gains over single-pair DPO plus an NLL term on positives.
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OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search
OASES co-trains a search policy and an outcome-aligned state evaluator so intermediate search steps get denser, outcome-consistent process rewards and beat strong RL baselines on multi-hop QA.