ACLArena: Agent Continue Learning in Multi-stage Post-training
ACLArena compares agent continual-learning methods and proposes replay plus routed LoRA experts to limit forgetting.
ACLArena is a framework for studying agent continual learning when capabilities are acquired in separate post-training stages. The authors analyze forgetting and generalization at both model and token levels, then compare multi-teacher on-policy distillation, self-distilled fine-tuning, and model merging. Their recipe combines offline replay of high-quality trajectories with a routed network of LoRA experts specialized via reinforcement learning. Experiments on four reasoning and agentic tasks, in-domain and out-of-domain, show improved retention of earlier skills while learning new ones.
- ACLArena studies forgetting and generalization at model and token levels.
- Compared methods include multi-teacher on-policy distillation, self-distilled fine-tuning, and model merging.
- The proposed recipe pairs offline replay of high-quality trajectories with routed LoRA experts trained by RL.
- Evaluation covers four reasoning and agentic tasks, in-domain and out-of-domain.
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From the original article
1 min read at huggingface.co
Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of training. Yet there is currently no well-established recipe for Agent Continual Learning (ACL), with little understanding of the trade-offs among existing integration paradigms.
By Haixin Wang, Xiaoxuan Wang, Junkai Zhang · huggingface.co
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