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.

Language agents increasingly rely on reusable skills, but post-failure repair is often handled by opaque one-shot reflection: a model generates a skill patch without explicitly maintaining how failure explanations relate to candidate repairs or how unsuccessful retests should influence later edits. We introduce RESKILL, a structured repair framework that maintains an explicit repair state across repair rounds. Given a failed rollout, the framework links failure hypotheses to candidate skill patches, selects local repairs through coverage-based attribution, retests the edited skill set in the environment, and uses retest outcomes to guide subsequent repair updates. The language model supplies structured repair factors, while the repair procedure records them, compares local skill patches by how well they address active failure explanations, and carries unsuccessful retest outcomes into later repair rounds. We evaluate RESKILL on ALFWorld and TextCraft across three model sizes under fixed repair budgets. RESKILL obtains the strongest final success in all six benchmark–model settings, improving average final success by 3.7 percentage points over direct repair and 3.3 points over hypothesis-conditioned repair. These results suggest that explicit attribution alone is insufficient; durable improvement emerges when attribution is integrated with repair selection and persistent retest-conditioned update.