arXiv (All)
open access
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions
Text language models (LMs) acquire broad linguistic and factual capabilities through large-scale pretraining, while comparable capabilities remain difficult to learn from speech alone. Recent work has shown that knowledge distillation from pretrained text LMs offers a promising way to transfer their
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LLM Post-Training: A Deep Dive Into Reasoning Large Language Models#1274281
- MOSCOPT: Mixture-of-Skills Collective Optimization for LLM Agents#1035610
- Where to Adapt Matters: Layer-Selective Fine-Tuning for Capability Retention#1248900
- Probing Factual Knowledge Transfer with Training Data Interventions#821286
- A Recipe for Long-Context Reasoning in Large Language Models via On-Policy Optimization and Distillation#998226
Record · ID 1342678
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive