SURF derives weight sampling rules from the arc-length CDF of the scalarization path to uniformly traverse the Pareto front in multi-objective optimization.
Projection optimization: A general framework for multi-objective and multi-group rlhf
3 Pith papers cite this work. Polarity classification is still indexing.
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GPRL carries a k-dimensional skew-symmetric preference structure into policy updates with per-dimension advantages and a drift monitor, yielding 56.51% length-controlled win rate on AlpacaEval 2.0 from Llama-3-8B-Instruct while outperforming SimPO and SPPO on other benchmarks.
Blackwell's Rao-Blackwell, Approachability, and Informativeness theorems provide frameworks for variance reduction, sequential decisions under uncertainty, and comparing information sources that remain relevant to AI.
citing papers explorer
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SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front
SURF derives weight sampling rules from the arc-length CDF of the scalarization path to uniformly traverse the Pareto front in multi-objective optimization.
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General Preference Reinforcement Learning
GPRL carries a k-dimensional skew-symmetric preference structure into policy updates with per-dimension advantages and a drift monitor, yielding 56.51% length-controlled win rate on AlpacaEval 2.0 from Llama-3-8B-Instruct while outperforming SimPO and SPPO on other benchmarks.
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The Theorems of Dr. David Blackwell and Their Contributions to Artificial Intelligence
Blackwell's Rao-Blackwell, Approachability, and Informativeness theorems provide frameworks for variance reduction, sequential decisions under uncertainty, and comparing information sources that remain relevant to AI.