Unverified50% confidenceFactExact time
Traditional TTS is trained with mel-spectrogram reconstruction loss or adversarial loss, and the optimization objective deviates from human subjective perception; MOS-type subjective metrics are not differentiable, so a learnable judge is suitable as a proxy reward for direct optimization via policy gradients.
1
Sources
50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSupervised fine-tuning (SFT) optimizes imitation loss rather than a behavioral safety objective, so it readily exhibits the phenomenon of "working within the training distribution but failing out of distribution."73% similarUnverifiedIn traditional approaches, reinforcement learning action and observation data is transmitted over ROS topics, which becomes a performance bottleneck during high-frequency training.70% similarUnverifiedTTS systems are trained to maximize naturalness and fluency, systematically eliminating uncertainty signals such as speech hesitations and pauses.70% similarUnverifiedKey technologies for long-horizon agents include state representation learning, goal alignment mechanisms, and error detection and recovery strategies.68% similarUnverifiedThe reason for using a correlation coefficient rather than simply comparing final loss values is that it can capture the dynamic consistency across the entire training run, which proves the numerical validity of the training process better than a single endpoint metric.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1115378API
curl https://kongchang.com/api/v1/knowledge/claims/1115378MCP
get_claim(id=1115378)