Unverified50% confidenceFactExact time
The core idea of the L2RR method is that the target data owner can provide limited aggregate feedback (such as accuracy) from a small held-out target set without exposing target prompts or rollout trajectories.
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11/3/2026
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UnverifiedL2RR learns a discrete sampling distribution over source prompts by optimizing a bilevel objective: maximizing accuracy on the held-out target dataset while penalizing those rollouts that provide little incremental learning information.76% similarUnverifiedL2RR was empirically evaluated on six mathematical reasoning benchmarks and three model scales, and the results show that L2RR improves target-domain performance while also improving rollout efficiency.69% similarUnverifiedPlacing constraints declared at the model capability layer can allow the toolchain to provide clear feedback at the earliest stage, rather than exposing difficult-to-understand errors at runtime.65% similarUnverifiedThe research team proposes an improved preference optimization formulation that explicitly aligns with the characteristics of few-step generation and can directly remove target concepts within the FSD framework.65% similarUnverifiedAn explicit confidence target means the user specifies a target value p in the prompt, and the model accordingly directly chooses whether to answer or abstain, with the goal that the accuracy on answered questions reaches at least p.64% similar
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