Cross-Domain Hybrid OPD for Generalizable Search Agents

TL;DR - A technical report on the training framework behind the Yuanbao search agent, which combines agentic RL with cross-domain expert on-policy distillation to build a specialized search agent without paying the usual "alignment tax" on general capabilities.

• Built on the Hunyuan3 architecture; agentic RL trains autonomous multi-step planning and iterative retrieval over dynamic information sources.
• The core contribution is a cross-domain expert On-Policy Distillation (OPD) pipeline: experts covering complementary general-purpose domains are distilled into the search-specialized student to restore and further improve broad capability.
• Framing: specialization and generality are treated as jointly optimizable rather than competing, directly targeting the alignment tax seen when tuning LLMs for narrow search behaviors.
• Reported experiments claim competitive search performance alongside consistent general-capability gains; the report does not provide specific benchmark numbers in the abstract, so magnitude of improvement is unverified here.