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RoVR-GSPO is introduced, a dual-channel robust optimizer that addresses group-relative policy optimization failure modes separately and shows consistent improvements over GSPO, while controlled perturbation studies demonstrate stronger robustness to reward contamination and token-ratio anomalies.
Sparse Probing and Outcome-calibrated Targets OPD is introduced, which addresses two coupled decisions, where to probe and what to distill, through an acquisition--exploration--exploitation procedure and demonstrates the effectiveness of SPOT in improving reasoning performance while balancing solution quality and cover...
WDL-OPD is introduced, a mixture-constrained co-training method with two trainable policies that shows that freezing the auxiliary recovers an anchor-plus-contrast proxy target closely related to OPD$^2$ and W2S-OPD, whereas joint training creates branch-level degrees of freedom that a static delta cannot express.
We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective wisdom of multiple models with diverse strengths. LLM-PeerReview is built on a novel, peer-review-inspired framework that offers a transpar...
LLMODE is proposed, a token-efficient framework for irregular spatio-temporal forecasting with a frozen LLM backbone that shows competitive overall performance, with clearer advantages under sparse or dynamically complex irregular sampling.
Experimental results show that DEFT-RLVR improves AD reasoning while preserving or even enhancing general visual capabilities, and Autonomous-Driving Multiple-Choice Question (AD-MCQ) provides a flexible, scalable, and extensible foundation for future research on verifiable AD reasoning.
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