Topic
preference alignment
AAPA: Adversarially Anchored Preference Alignment Enhances LLM Post-Training Performance
Researchers propose AAPA, a plug-in framework that adds a sentence-level adversarial anchoring signal to existing post-training objectives for large language models. Experiments on instruction-following benchmarks show consistent improvements, with staged AAPA achieving 5.77% gain on Qwen3-0.6B and 3.75% on Qwen3-4B over a strong GRPO baseline.
TuneJury: Open Metric Improves Music Generation Preference Alignment
Researchers introduce TuneJury, an open metric for improving music generation preference alignment. The model predicts preference scores from text prompts and audio clips, trained on diverse human-preference labels, and supports data filtering and post-hoc calibration.
CHILLGuard: Fine-Grained Chinese LLM Safety Guardrail with Scalable Data and Preference Alignment
Researchers introduce CHILLGuard, a dedicated Chinese LLM content safety guardrail featuring a 5-macro, 31-micro category risk taxonomy. The system uses a scalable multi-stage data construction pipeline to create the CHILLGuardTrain dataset (405,007 samples) and achieves a 15.92% F1 score improvement over Qwen3Guard-8B-Strict via Model-aware Direct Preference Optimization.