[NeurIPS 2024] SimPO: Simple Preference Optimization with a Reference-Free Reward
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Updated
Feb 16, 2025 - Python
[NeurIPS 2024] SimPO: Simple Preference Optimization with a Reference-Free Reward
[Paper][ACL 2024 Findings] Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Video Generation Benchmark
Code for "ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing"
DPO-Shift: Shifting the Distribution of Direct Preference Optimization
[NeurIPS 2024] Official code of $\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$
Source code for "A Dense Reward View on Aligning Text-to-Image Diffusion with Preference" (ICML'24).
[ICCV 2025] Official repository of "Mitigating Object Hallucinations via Sentence-Level Early Intervention".
[ICML 25] "Preference Optimization for Combinatorial Optimization Problems"
[ICLR 2025] Official code of "Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization"
[ICLR 2026] Official repository of "Uni-DPO: A Unified Paradigm for Dynamic Preference Optimization of LLMs".
[ICLR 2025] Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization
[ICML 2025] TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization
[NeurIPS 2025] Ranking-based Preference Optimization for Diffusion Models from Implicit User Feedback
LLM-Driven Preference Data Synthesis for Proactive Prediction of the User’s Next Utterance in Human–Machine Dialogue
A living, evidence-based catalog of benchmarks for personalized LLMs and AI agents—covering preference alignment, long-term memory, tool use, safety, privacy, and multimodal adaptation.
Convex Optimization for Alignment and Preference Learning on a Single GPU (lab repo at pilancilab/COALA)
SFT + DPO fine-tuning of a small open LLM (Qwen2.5) for grounded, calibrated credit-risk reasoning. QLoRA, TRL, PEFT, DPO, FastAPI/Gradio/Docker.
Factual Preference Alignment is a research and engineering framework for studying and improving factual alignment in preference-optimized Large Language Models (LLMs).
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