UCLA Releases ACLArena: A Framework for Agent Continual Learning in Multi-Stage Post-Training
UCLA-SCAI · hf · 2026-09-23
UCLA-SCAI introduced ACLArena, a framework to systematically study Agent Continual Learning (ACL), addressing the lack of established recipes for integrating multiple capabilities across sequential training stages.
Key contributions:
- A sequential training pipeline with analyses of forgetting and generalization at both model and token levels;
- A systematic comparison of multi-teacher on-policy distillation, self-distilled fine-tuning, and model merging for recovering old capabilities while preserving new ones;
- A new ACL recipe combining offline replay over high-quality trajectories with a routed network of LoRA experts specialized via RL, substantially improving cross-domain learning;
- Extensive experiments on four reasoning and agentic tasks under in-domain and out-of-domain settings.
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