Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, short

Summary

The TimePro-RL framework addresses limitations in Large Audio-Language Models' (LALMs) fine-grained temporal perception, specifically their ability to infer event onset and offset timestamps. Current LALMs excel at semantic recognition but struggle with precise temporal boundaries, which are crucial for tasks like audio grounding and sound event detection. TimePro-RL integrates temporal information by encoding timestamps as embeddings and interleaving them within audio feature sequences as "Audio-Side Time Prompts." This provides explicit temporal coordinates to the model. Furthermore, the framework employs Reinforcement Learning (RL) post-training, following Supervised Fine-Tuning (SFT), to directly optimize temporal alignment performance using an advantage-driven adaptive temporal reward. Experiments confirm that TimePro-RL significantly improves performance across various audio temporal tasks, including audio grounding, sound event detection, and dense audio captioning.

Key takeaway

For research scientists developing or deploying Large Audio-Language Models, consider integrating explicit temporal prompting and reinforcement learning for fine-grained temporal tasks. Your models will achieve more precise onset and offset predictions for sound events, improving performance in applications like audio grounding and dense audio captioning. This approach directly addresses a key limitation in current LALMs' temporal understanding.

Key insights

TimePro-RL enhances LALMs' temporal perception by integrating explicit time prompts and optimizing with reinforcement learning.

Principles

• Explicit temporal cues improve LALM localization.
• RL optimizes time-boundary prediction deviations.
• Interleaving timestamps reduces reasoning difficulty.

Method

Encode timestamps as embeddings and interleave them into audio feature sequences as temporal coordinates. Then, apply Reinforcement Learning post-training with an advantage-driven adaptive temporal reward to optimize temporal alignment.

In practice

• Extend tokenizer with Timestamp Tokens.
• Partition audio into token sequences.
• Insert Timestamp Tokens at fixed time points.

Topics

• Large Audio-Language Models
• Fine-grained Temporal Perception
• Audio-Side Time Prompt
• Reinforcement Learning
• TimePro-RL Framework

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

Related on AIssential

• Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt — TimePro-RL enhances LALMs' temporal perception by integrating timestamp embeddings and optimizing with reinforcement learning.
• Context-Aware RL for Agentic and Multimodal LLMs — ContextRL uses an indirect RL objective to reward fine-grained grounding by selecting supporting contexts for query-answer pairs.
• DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training — Asynchronous RL for LLMs can achieve high efficiency and convergence by maintaining policy consistency and dynamic resource orchestration.
• Acoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models — Integrating trainable audio-side prompts with text-side prompt tuning significantly enhances few-shot adaptation in Audio-Language Models.
• Covertly improving intelligibility with data-driven adaptations of speech timing — Targeted speech rate adjustments, not global slowing, covertly improve intelligibility across diverse listener groups.
• Continuous Audio Thinking for Large Audio Language Models — CoAT enables LALMs to retain and organize acoustic information in a continuous latent space, improving understanding without added inference cost.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.