arXiv (All)
open access
Train4Merge: A Controlled Single-Teacher Study of RL vs. SFT Teachers for OPD-Based Model Merging
Domain experts trained from a shared checkpoint can be merged into one model through on-policy distillation (OPD), where they act as teachers supervising a student on its own trajectories. One upstream choice is rarely examined: whether to build each expert with supervised fine-tuning (SFT) or reinf
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
- Contrastive On-Policy Distillation#1143849
- Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation#1083271
- Verify Before You Distill: Prompt-Level Teacher Gating for On-Policy Distillation#925371
- Verify Before You Distill: Prompt-Level Teacher Gating for On-Policy Distillation#921135
- TV-Regulated OPD: Direction Matters in On-Policy Distillation#985701
Record · ID 1116634
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