24 citations · 120 across the 27 of their papers we have counts for
Showing 2025Show all
3 papers · 1 filter
cs.CL2025
Specification Self-Correction: Mitigating In-Context Reward Hacking Through Test-Time Refinement
Víctor Gallego
Language models (LMs) are susceptible to in-context reward hacking, where they exploit flaws in tainted or faulty written specifications or rubrics to achieve high scores without f…
cs.CL2025
Configurable Preference Tuning with Rubric-Guided Synthetic Data
Víctor Gallego
Models of human feedback for AI alignment, such as those underpinning Direct Preference Optimization (DPO), often bake in a singular, static set of preferences, limiting adaptabili…
cs.CL2025
MetaSC: Test-Time Safety Specification Optimization for Language Models
Víctor Gallego
We propose a novel dynamic safety framework that optimizes language model (LM) safety reasoning at inference time without modifying model weights. Building on recent advances in se…