Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models

arXiv:2608.18132v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are typically built through a multi-stage pipeline consisting of cross-modal alignment, supervised fine-tuning (SFT), and preference optimization. This pipeline assumes that adapting an LLM to a new modality requires extensive task-specific supervision. However, pretrained LLMs already possess strong reasoning and instruction-following abilities. As LLMs evolve rapidly, an important question remains: can we efficiently transfer these capabilities to a new modality with minimal intervention, and is alignment alone sufficient for building a multimodal model? We introduce an Instruction-Free Alignment-Only large audio-language model (LALM) that keeps both the audio encoder and the LLM fully frozen, learning only a lightweight projector. Borrowing insights from AzeroS [1], we train on (audio, response) pairs from Self-Generated Data Construction, where an LLM expands captions into free-form responses without explicit task instructions. Across MMAU, MMAR, MMSU, and MMAU-Pro, our approach matches or surpasses heavily post-trained baselines using substantially less data. By keeping the LLM frozen, our model preserves its native instruction-following competence and can port seamlessly across model generations. Our results suggest that competitive MLLM can emerge from alignment alone, reducing multimodal extension to a lightweight projector-training problem that generalizes across modalities and adapts rapidly to each new LLM release.

Abstract:Multimodal large language models (MLLMs) are typically built through a multi-stage pipeline consisting of cross-modal alignment, supervised fine-tuning (SFT), and preference optimization. This pipeline assumes that adapting an LLM to a new modality requires extensive task-specific supervision. However, pretrained LLMs already possess strong reasoning and instruction-following abilities. As LLMs evolve rapidly, an important question remains: can we efficiently transfer these capabilities to a new modality with minimal intervention, and is alignment alone sufficient for building a multimodal model? We introduce an Instruction-Free Alignment-Only large audio-language model (LALM) that keeps both the audio encoder and the LLM fully frozen, learning only a lightweight projector. Borrowing insights from AzeroS [1], we train on (audio, response) pairs from Self-Generated Data Construction, where an LLM expands captions into free-form responses without explicit task instructions. Across MMAU, MMAR, MMSU, and MMAU-Pro, our approach matches or surpasses heavily post-trained baselines using substantially less data. By keeping the LLM frozen, our model preserves its native instruction-following competence and can port seamlessly across model generations. Our results suggest that competitive MLLM can emerge from alignment alone, reducing multimodal extension to a lightweight projector-training problem that generalizes across modalities and adapts rapidly to each new LLM release.

Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)

arXiv:2608.18132 [cs.CL]

(or arXiv:2608.18132v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.18132 Focus to learn more arXiv-issued DOI via DataCite

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Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models

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