Recent advances in Large Reasoning Models (LRMs) are reshaping reasoning-intensive reranking, where step-by-step reasoning enables multi-faceted relevance judgments. To scale LRMs, existing approaches distill chain-of-thought traces from a teacher LRM into a student reranker via Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL). However, this pipeline is misaligned with the distillation objective: teacher traces are too dense for direct imitation, while RL exploration is too sparse on hard cases. To this end, we propose SARD (Self-Adaptive Reinforced Distillation), a tri-mode RL framework that recasts teacher reasoning from a fixed supervision target into an adaptive resource the student draws on only when its own reasoning falls short. Triggered by rollout outcomes, SARD organizes training into three modes: Free Exploration, where the student reasons independently; Guided Exploration, which supplies reusable in-context guidance abstracted from teacher traces when free rollouts fail; and Teacher-Guided Repair, where the teacher diagnoses failures and revises, re-selects, or appends guidance, falling back to teacher trajectories only as a last resort. Experiments on reasoning-intensive reranking benchmarks show SARD outperforms strong reranking and SFT-then-RL baselines, with clear gains on difficult instances and under-represented reasoning patterns.

The advances of large foundation models necessitate wide-coverage, low-cost, and zero-contamination benchmarks. Despite continuous exploration of language model evaluations, comprehensive studies on the evaluation of Large Multi-modal Models (LMMs) remain limited. In this work, we introduce LMMS-EVAL, a unified and standardized multimodal benchmark framework with over 50 tasks and more than 10 models to promote transparent and reproducible evaluations. Although LMMS-EVAL offers comprehensive coverage, we find it still falls short in achieving low cost and zero contamination. To approach this evaluation trilemma, we further introduce LMMS-EVAL LITE, a pruned evaluation toolkit that emphasizes both coverage and efficiency. Additionally, we present Multimodal LIVEBENCH that utilizes continuously updating news and online forums to assess models’ generalization abilities in the wild, featuring a low-cost and zero-contamination evaluation approach. In summary, our work highlights the importance of considering the evaluation trilemma and provides practical solutions to navigate the trade-offs in evaluating large multi-modal models, paving the way for more effective and reliable benchmarking of LMMs.