Two-phase distillation for multitask models
A paper published on June 30, 2026 studies consolidating reinforcement-learning specialists into a multitask model through off-policy and on-policy distillation.
Source: Distilação em duas fases para LLMs agentic multitarefa (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on June 30, 2026, the paper studies consolidating specialist models trained with reinforcement learning for specific tasks into a single multitask model. Its approach uses off-policy distillation to initialize the model and on-policy distillation to refine it. The paper presents this as an alternative to training on combined tasks and examines a limitation of off-policy distillation in multitask settings.
A company developing agents could test this strategy on a controlled set of tasks: define task-level metrics, log specialist trajectories and outcomes, and compare the two distillation phases with combined-task training. An evaluation pipeline and dashboard could track quality, regressions, and cost before deciding whether consolidation is suitable for production.
How the consultancy can help
Wendelmaques can assess the opportunity and data and evaluation requirements, scope an implementation of the training and monitoring pipeline, and support its operation on the company's infrastructure.
Next step
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