Pref-CTRL trains a multi-objective value function on preferences to guide representation editing for LLM alignment, outperforming RE-Control on benchmarks with better out-of-domain generalization.
Towards Scalable Automated Alignment of LLMs: A Survey
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ReAlignFit uses chemical induced fit bias and subgraph information bottleneck to dynamically align molecular substructure representations and improve stability on rule-shifted and scaffold-shifted data.
Qwen2.5 LLMs scale pre-training data to 18 trillion tokens and apply multistage reinforcement learning, achieving competitive performance on benchmarks with models up to 5 times larger.
citing papers explorer
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Pref-CTRL: Preference Driven LLM Alignment using Representation Editing
Pref-CTRL trains a multi-objective value function on preferences to guide representation editing for LLM alignment, outperforming RE-Control on benchmarks with better out-of-domain generalization.
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Representational Alignment with Chemical Induced Fit for Molecular Relational Learning
ReAlignFit uses chemical induced fit bias and subgraph information bottleneck to dynamically align molecular substructure representations and improve stability on rule-shifted and scaffold-shifted data.
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Qwen2.5 Technical Report
Qwen2.5 LLMs scale pre-training data to 18 trillion tokens and apply multistage reinforcement learning, achieving competitive performance on benchmarks with models up to 5 times larger.