Verified text rewards turn audio models into robotic answering machines that lose all emotional nuance.
Audio reasoning models fail when they are treated like pure logic puzzles. Using reinforcement learning from human feedback instead of discrete text rewards preserves the essential tone and pacing of human speech. The transition to human-centric rewards prevents the model from sounding like a monotone computer. This strategy prioritizes the emotional how over the literal what of a response. Building natural voice assistants now requires moving away from the reasoning only trend and back toward human perception.
Step-Audio-R1.5 Technical Report
arXiv · 2604.25719
Recent advancements in large audio language models have extended Chain-of-Thought (CoT) reasoning into the auditory domain, enabling models to tackle increasingly complex acoustic and spoken tasks. To elicit and sustain these extended reasoning chains, the prevailing paradigm -- driven by the success of text-based reasoning models -- overwhelmingly relies on Reinforcement Learning with Verified Rewards (RLVR). However, as models are strictly optimized to distill rich, continuous auditory context