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20242026
most citedAdvantage Alignment Algorithms

1 citations · 1 across the 17 of their papers we have counts for

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cs.LG2025

Learning Robust Social Strategies with Large Language Models

Dereck Piche, Mohammed Muqeeth, Milad Aghajohari +3

As agentic AI becomes more widespread, agents with distinct and possibly conflicting goals will interact in complex ways. These multi-agent interactions pose a fundamental challeng…

cs.LG2025

Generative Adversarial Post-Training Mitigates Reward Hacking in Live Human-AI Music Interaction

Yusong Wu, Stephen Brade, Aleksandra Teng Ma +6

Most applications of generative AI involve a sequential interaction in which a person inputs a prompt and waits for a response, and where reaction time and adaptivity are not impor…

cs.LG2025

Evolution Strategies at the Hyperscale

Bidipta Sarkar, Mattie Fellows, Juan Agustin Duque +17

Evolution Strategies (ES) is a class of powerful black-box optimisation methods that are highly parallelisable and can handle non-differentiable and noisy objectives. However, naïv…

cs.SD2025

Streaming Generation for Music Accompaniment

Yusong Wu, Mason Wang, Heidi Lei +5

Music generation models can produce high-fidelity coherent accompaniment given complete audio input, but are limited to editing and loop-based workflows. We study real-time audio-t…

cs.SD2025

Adaptive Accompaniment with ReaLchords

Yusong Wu, Tim Cooijmans, Kyle Kastner +10

Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expressive output but are not able to genera…

cs.SD2025

FLAM: Frame-Wise Language-Audio Modeling

Yusong Wu, Christos Tsirigotis, Ke Chen +5

Recent multi-modal audio-language models (ALMs) excel at text-audio retrieval but struggle with frame-wise audio understanding. Prior works use temporal-aware labels or unsupervise…

cs.LG2025

Adaptive Computation Pruning for the Forgetting Transformer

Zhixuan Lin, Johan Obando-Ceron, Xu Owen He +1

The recently proposed Forgetting Transformer (FoX) incorporates a forget gate into softmax attention and has shown consistently better or on-par performance compared to the standar…

cs.LG2025

Forgetting Transformer: Softmax Attention with a Forget Gate

Zhixuan Lin, Evgenii Nikishin, Xu Owen He +1

An essential component of modern recurrent sequence models is the forget gate. While Transformers do not have an explicit recurrent form, we show that a forget gate can be naturall…