Víctor Gallego

Research Interests

My recent work centers on making large language models safer and more steerable. I develop methods for configurable preference tuning using synthetic data and rubric-guided generation, enabling fine-grained control over LLM behavior. A recurring theme is test-time adaptation: rather than relying solely on training, I explore how models can self-correct at inference time—refining safety specifications on the fly and mitigating reward hacking without retraining. I also investigate self-critique and model merging as defenses against jailbreak attacks, and more recently, how LLMs can distill user feedback into persistent memory to improve over successive interactions.

Selected Publications

2026

Distilling Feedback into Memory-as-a-Tool

V Gallego. ICLR 2026 Workshop on Memory for LLM-Based Agentic Systems · arXiv

2025

Specification Self-Correction: Mitigating In-Context Reward Hacking Through Test-Time Refinement

V Gallego. SCALR Workshop @ COLM 2025 · arXiv

Configurable Preference Tuning with Rubric-Guided Synthetic Data

V Gallego. ICML 2025 Workshop on Models of Human Feedback for AI Alignment · arXiv

MetaSC: Test-Time Safety Specification Optimization for Language Models

V Gallego. ICLR 2025 Workshop on Foundation Models in the Wild · arXiv

2024

Refined Direct Preference Optimization with Synthetic Data for Behavioral Alignment of LLMs

V Gallego. LOD 2024: 10th International Conference on Machine Learning, Optimization, and Data Science · arXiv

Protecting Classifiers from Attacks

V Gallego, R Naveiro, A Redondo, D Ríos Insua, F Ruggeri. Statistical Science 39(3), 2024 · arXiv

Merging Improves Self-Critique Against Jailbreak Attacks

V Gallego. ICML 2024 Workshop on Foundation Models in the Wild · arXiv

Configurable Safety Tuning of Language Models with Synthetic Preference Data

V Gallego. 3rd Workshop on Practical Deep Learning: Towards Efficient and Reliable LLMs @ IEEE CAI 2024 · arXiv

2023

Adversarial Machine Learning: Bayesian Perspectives

D Rios Insua, R Naveiro, V Gallego, J Poulos. Journal of the American Statistical Association 118(543), 2023 — 56 citations · arXiv

ZYN: Zero-Shot Reward Models with Yes-No Questions for RLAIF

V Gallego. arXiv:2308.06385 · arXiv

2022

Personalizing Text-to-Image Generation via Aesthetic Gradients

V Gallego. NeurIPS 2022, Workshop on ML for Creativity and Design · arXiv

Current Advances in Neural Networks

V Gallego, D Rios Insua. Annual Review of Statistics and Its Application 9, 2022 — 31 citations

Adversarial Risk Analysis: An Overview

D Banks, V Gallego, R Naveiro, D Ríos Insua. WIREs Computational Statistics 14(1), 2022 — 49 citations · arXiv

2021 & earlier

AI in Drug Development: A Multidisciplinary Perspective

V Gallego, R Naveiro, C Roca, D Rios Insua, NE Campillo. Molecular Diversity 25(3), 2021 — 100 citations

Reinforcement Learning under Threats

V Gallego, R Naveiro, D Rios Insua. AAAI 2019 — 42 citations · arXiv

Stochastic Gradient MCMC with Repulsive Forces

V Gallego, D Rios Insua. NeurIPS 2018, Workshop on Bayesian Deep Learning — 46 citations · arXiv

Full list → Google Scholar

Education

PhD in Statistics & Operations Research, Universidad Complutense de Madrid, 2021
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
Supervised by David Ríos Insua (ICMAT, Royal Academy of Sciences) and David Gómez-Ullate (ICMAT, UCA)

MSc in Mathematical Engineering, UCM

Double Degree in Mathematics & Computer Science, UCM

Now

Research Interests

Selected Publications

Education