Legal judgment prediction (LJP) is important for supporting judicial decision-making and improving judicial efficiency. Existing LLM-based approaches often rely on manually specified reasoning pipelines or external legal structures, while feedback-based methods typically provide generic signals of correctness or coherence rather than case-specific legal supervision. We propose IALR: Instance-Adaptive Legal Rubric Supervision for Legal Judgment Prediction. For each training instance, IALR contrasts model responses of varying quality and distills their legally consequential failures into a weighted Instance-Adaptive Legal Rubric. These rubrics convert judgment-relevant conditions into discriminative preference signals for Direct Preference Optimization, enabling the model to learn charge prediction without following a predefined reasoning paradigm. At inference time, IALR requires no additional evaluator or correction module. Experiments on four Chinese criminal charge-prediction benchmarks show that IALR consistently improves Qwen2.5-7B, yielding average absolute accuracy gains of 5.68% in-domain and 8.73% out-of-domain. These results demonstrate the effectiveness and cross-domain transferability of Instance-Adaptive Legal Rubric supervision. The code and data are available at https://github.com/Baii668/IALR.git.

To improve the performance of large language models (LLMs), researchers have explored providing LLMs with textual task-solving experience via prompts. However, they rely on manual efforts to acquire and apply such experience for each task, which is not feasible for the growing demand for LLMs and the variety of user questions.To address this issue, we design a lifelong autonomous experiential learning framework based on LLMs to explore whether LLMs can imitate human ability for learning and utilizing experience. It autonomously learns and accumulates experience through experience transfer and induction, categorizing the types of input questions to select which accumulated experience to employ for them.Experimental results on six widely used NLP datasets show that our framework performs reliably in each intermediate step and effectively improves the performance of GPT-3.5 and GPT-4. This validates the feasibility of using LLMs to mimic human experiential learning and application capabilities, offering a new path worth further exploration for the evolution of machine intelligence. Additionally, we provide a detailed analysis of the behavior of our framework at each step.We will open source codes after the acceptance, fostering open research in the NLP community and beyond.