LSC-DPO: Learning-Signal-Controlled Direct Preference Optimization

cs.AI

Submitted: 2026-10-06

Updated: 2026-10-06

Comments: 27 pages, 15 figures, 14 tables

Code: https://github.com/handan-liu-lab/lsc-dpo

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Training Verifiers to Solve Math Word Problems
• UltraFeedback: Boosting Language Models with Scaled AI Feedback
• Safe RLHF: Safe Reinforcement Learning from Human Feedback
• Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
• Learn Your Reference Model for Real Good Alignment
• The Llama 3 Herd of Models
• GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements
• Measuring Massive Multitask Language Understanding
• From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
• WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
• Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
• Proximal Policy Optimization Algorithms
• AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization
• Qwen3 Technical Report
• RRHF: Rank Responses to Align Language Models with Human Feedback without tears
• SLiC-HF: Sequence Likelihood Calibration with Human Feedback
• Fine-Tuning Language Models from Human Preferences

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