2.2k citations · 3.2k across the 79 of their papers we have counts for
18 papers · 1 filter
RRPO: Robust Reward Policy Optimization for LLM-based Emotional TTS
Cong Wang, Changfeng Gao, Yang Xiang +7
Differentiable reinforcement learning (RL) frameworks like DiffRO offer a powerful approach for controllable text-to-speech (TTS), but are vulnerable to reward hacking, particularl…
PrismAudio: Decomposed Chain-of-Thoughts and Multi-dimensional Rewards for Video-to-Audio Generation
Huadai Liu, Kaicheng Luo, Wen Wang +6
Video-to-Audio (V2A) generation requires balancing four critical perceptual dimensions: semantic consistency, audio-visual temporal synchrony, aesthetic quality, and spatial accura…
Explore the Reinforcement Learning for the LLM based ASR and TTS system
Changfeng Gao, Yabin Li, Keyu An +4
In recent years, large language models (LLMs) have played an important role in automatic speech recognition (ASR) and text-to-speech (TTS) systems. While reinforcement learning (RL…
Eliminating stability hallucinations in llm-based tts models via attention guidance
ShiMing Wang, ZhiHao Du, Yang Xiang +6
This paper focuses on resolving stability hallucinations (e.g., repetitive or omitted speech) in LLM-based Text-to-Speech (TTS) models by improving and leveraging the attention mec…
MELA-TTS: Joint transformer-diffusion model with representation alignment for speech synthesis
Keyu An, Zhiyu Zhang, Changfeng Gao +7
This work introduces MELA-TTS, a novel joint transformer-diffusion framework for end-to-end text-to-speech synthesis. By autoregressively generating continuous mel-spectrogram fram…
Fun-ASR Technical Report
Keyu An, Yanni Chen, Zhigao Chen +35
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep in…
MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
Fan Zhang, Zebang Cheng, Chong Deng +18
Recent advances in multimodal large language models (MLLMs) have catalyzed transformative progress in affective computing, enabling models to exhibit emergent emotional intelligenc…
SpeakerLM: End-to-End Versatile Speaker Diarization and Recognition with Multimodal Large Language Models
Han Yin, Yafeng Chen, Chong Deng +6
The Speaker Diarization and Recognition (SDR) task aims to predict "who spoke when and what" within an audio clip, which is a crucial task in various real-world multi-speaker scena…
AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
Yunjie Ji, Xiaoyu Tian, Sitong Zhao +5
We present AM-Thinking-v1, a 32B dense language model that advances the frontier of reasoning, embodying the collaborative spirit of open-source innovation. Outperforming DeepSeek-…
Not All Correct Answers Are Equal: Why Your Distillation Source Matters
Xiaoyu Tian, Yunjie Ji, Haotian Wang +5
Distillation has emerged as a practical and effective approach to enhance the reasoning capabilities of open-source language models. In this work, we conduct a large-scale empirica…
DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Although large language models (LLMs) have recently achieved remarkable performance on various complex reasoning benchmarks, the academic community still lacks an in-depth understa…
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Despite significant advances in long-context reasoning by large language models (LLMs), primarily through Online Reinforcement Learning (RL) methods, these approaches incur substan…
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning
Cheng Wen, Tingwei Guo, Shuaijiang Zhao +2
Recent work shows that reinforcement learning(RL) can markedly sharpen the reasoning ability of large language models (LLMs) by prompting them to "think before answering." Yet whet…
Leveraging Reasoning Model Answers to Enhance Non-Reasoning Model Capability
Haotian Wang, Han Zhao, Shuaiting Chen +5
Recent advancements in large language models (LLMs), such as DeepSeek-R1 and OpenAI-o1, have demonstrated the significant effectiveness of test-time scaling, achieving substantial…
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study
Yunjie Ji, Sitong Zhao, Xiaoyu Tian +5
Enhancing the reasoning capabilities of Large Language Models (LLMs) with efficiency and scalability remains a fundamental challenge in artificial intelligence research. This paper…
Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning
Ruoxi Xu, Yunjie Ji, Boxi Cao +7
Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domai…
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Recent advances in large language models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where extended reasoning processes sub…
1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training
Han Zhao, Haotian Wang, Yiping Peng +5
The AM-DeepSeek-R1-Distilled is a large-scale dataset with thinking traces for general reasoning tasks, composed of high-quality and challenging reasoning problems. These problems…