Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization
Abstract
Large Audio Language Models (LALMs) have made rapid progress on standardized benchmarks, yet their deployment in practical media workflows, curation, archival indexing, and content distribution remains largely unrealized. We identify automated audio chapterization, the task of segmenting continuous audio streams into thematically coherent chapters, as a demanding and commercially consequential setting that exposes this gap. Chapterization is challenging because boundaries are defined less by objective acoustic events than by subjective editorial judgment, requiring models to reason sequentially over long acoustic contexts and approximate creator-authored boundary decisions. We present AudioChaps, a post-training framework for aligning end-to-end LALMs for this task via Group Relative Policy Optimization (GRPO) guided by Chain-of-Thought (CoT) reasoning. To support training and evaluation, we curate three datasets: AudioChaps-Alignment, derived from creator-annotated chapter boundaries on YouTube; AudioChaps-CoT, which provides structured supervision for well-formatted, high-quality, and evidence-grounded boundary reasoning; and AudioChaps-Eval, a held-out benchmark for audio chapterization. Applying GRPO directly without a Supervised Fine-Tuning (SFT) cold start, AudioChaps-R1-Zero already improves average F1 by 33 points over the state-of-the-art LALM Audio-Flamingo-3-Think. The AudioChaps framework produces our final aligned LALM, AudioChaps-R1, which improves average F1 by 49 points. These results demonstrate that GRPO-trained LALMs can reliably transform unstructured auditory streams into navigable, structured media. Our code, models, and dataset resources will be released upon acceptance at https://github.com/ta012/AudioChaps.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a well-executed study on aligning LALMs for audio chapterization using a novel synthetic CoT dataset and GRPO, demonstrating significant performance gains and contributing valuable resources to the field.
Methodology
The paper proposes AudioChaps, a framework for aligning Large Audio Language Models (LALMs) for automated audio chapterization. The core methodological contribution is a two-stage post-training pipeline: (1) Supervised Fine-Tuning (SFT) on a synthetic Chain-of-Thought (CoT) dataset (AudioChaps-CoT) to establish a structured reasoning format, and (2) Group Relative Policy Optimization (GRPO) to calibrate boundary decisions against creator-authored annotations. The construction of the CoT dataset via an "audio-to-text modality bridge" using a stronger model (Step-Audio-R1) to generate perception logs and reasoning traces is a notable engineering approach to address the lack of reasoning data for subjective audio tasks. The use of GRPO with rule-based rewards (format and accuracy) is a standard application of recent RL techniques but applied effectively to the audio domain. The approach is sound and addresses a specific gap in LALM capabilities (subjective segmentation vs. objective event detection).
Results & evaluation
The experimental setup is robust. The authors curate three datasets (Alignment, CoT, Eval) stratified across four acoustic regimes (structured speech, dynamic media, gaming, music). They evaluate on both clip-level binary classification and full-length recording chapter detection. The results show significant improvements over the base model (AF3-Think-8B) and a larger baseline (Step-Audio-R1-32B). The ablation studies effectively isolate the contributions of SFT and GRPO, demonstrating that SFT improves recall while GRPO improves precision. The comparison with an ASR-LLM cascade highlights the advantage of end-to-end audio processing for non-speech cues. The inclusion of human evaluation for reasoning quality adds value. The statistical significance testing via bootstrap is a good practice.
Reproducibility
The paper provides detailed descriptions of the dataset construction, training prompts, and hyperparameters. The code and models are promised upon acceptance. The use of standard open-source models (AF3-Think, MOSS-Think) as backbones enhances reproducibility. The synthetic data generation pipeline is described clearly enough to be replicated.
Limitations
The primary limitation is the reliance on synthetic CoT data, which may contain biases or errors from the teacher model. The task is formulated as a binary boundary detection within a window, which may not capture global context as effectively as native long-context models. The evaluation is limited to YouTube content, which may not generalize to all audio types (e.g., pure music without speech, highly noisy environments). The "creator-annotated" ground truth is a proxy for editorial judgment and may vary in quality or consistency.
Broader impact
This work contributes to the practical deployment of LALMs in media workflows, enabling better content navigation and archival indexing. It demonstrates the potential of RL alignment for subjective audio understanding tasks. The released datasets and benchmarks will facilitate further research in audio chapterization and reasoning. The paper presents a well-executed study on aligning LALMs for audio chapterization using a novel synthetic CoT dataset and GRPO, demonstrating significant performance gains and contributing valuable resources to the field.
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