Search Results
IPO: Interpretable Prompt Optimization for Vision-Language Models
Pre-trained vision-language models like CLIP have remarkably adapted to various downstream tasks.
IPO: Your Language Model is Secretly a Preference Classifier
Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences.
SCQPTH: an efficient differentiable splitting method for convex quadratic programming
We present SCQPTH: a differentiable first-order splitting method for convex quadratic programs.
Self-Play Preference Optimization for Language Model Alignment
In this paper, we propose a self-play-based method for language model alignment, which treats the problem as a constant-sum two-player game aimed at identifying the Nash equilibrium policy.
Sample-Efficient Alignment for LLMs
The results demonstrate that SEA achieves highly sample-efficient alignment with oracle's preferences, outperforming recent active exploration methods for LLMs.
Evidence of Crowding on Russell 3000 Reconstitution Events
We develop a methodology which replicates in great accuracy the FTSE Russell indexes reconstitutions, including the quarterly rebalancings due to new initial public offerings (IPOs).
Efficient Adversarial Training in LLMs with Continuous Attacks
We propose a fast adversarial training algorithm (C-AdvUL) composed of two losses: the first makes the model robust on continuous embedding attacks computed on an adversarial behaviour dataset; the second ensures the usefulness of the final model by fine-tuning on utility data.
An Empirical Study of Capital Asset Pricing Model based on Chinese A-share Trading Data
This paper presents an empirical analysis of the capital asset pricing model using trading data for the Chinese A-share market from 2000 to 2019.
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
A fundamental challenge in reinforcement learning is to learn policies that generalize beyond the operating domains experienced during training.
Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step Optimization
However, while RL-free methods deliver satisfactory performance, they require significant data to develop a robust Supervised Fine-Tuned (SFT) model and an additional step to fine-tune this model on a preference dataset, which constrains their utility and scalability.
