Conditional crystal generation requires Large Language Models (LLMs) to produce Crystal Strings with discrete and continuous variables while satisfying composition, symmetry, and property constraints. Desired regions are often sparse in supervised data, while retraining for each objective preference is costly. We present a verifier-grounded framework combining label-free Test-Time Reinforcement Learning (TTRL) and Model Merging (MM). TTRL performs one batch-level, partially transductive adaptation per property using composition, symmetry, and learned band-gap rewards, without target reference structures. Property-specific experts are interpolated to navigate bounded symmetry–band-gap trade-offs without retraining intermediate policies. On MP-20, TTRL improves symmetry control, band-gap accuracy, and compositional fidelity over High-Precision SFT and Standard RL, and is competitive with diffusion and preference-aligned baselines. In a held-out high-band-gap region, TTRL generalizes to test prompts unseen during adaptation, reducing the mean squared error between surrogate-predicted and target band gaps by 52% relative to standard reinforcement learning under best-of-ten sampling. An independent band-gap predictor shows consistent gains, while density functional theory calculations further assess selected candidates. Similar gains with Qwen3.5-9B provide cross-backbone evidence. Model Merging recovers competitive two-objective trade-offs from two endpoint experts without additional RL, enabling efficient adaptation and preference control for structured scientific generation.

Text summarization tasks commonly employ Pre-trained Language Models (PLMs) to fit diverse standard datasets. While these PLMs excel in automatic evaluations, they frequently underperform in human evaluations, indicating a deviation between their generated summaries and human summarization preferences. This discrepancy is likely due to the low quality of fine-tuning datasets and the limited availability of high-quality human-annotated data that reflect true human preference. To address this challenge, we introduce a novel human summarization preference alignment framework AlignSum. This framework consists of three parts: Firstly, we construct a Data Pymarid with extractive, abstractive, and human-annotated summary data. Secondly, we conduct the Gaussian Resampling to remove summaries with extreme lengths. Finally, we implement the two-stage hierarchical fine-tuning with Data Pymarid after Gaussian Resampling. We apply AlignSum to PLMs on the human-annotated CNN/DailyMail and BBC XSum datasets. Experiments show that with AlignSum, PLMs like BART-Large surpass 175B GPT-3 in both automatic and human evaluations. This demonstrates that AlignSum significantly enhances the alignment of language models with human summarization preferences.