ACLArena quantifies catastrophic forgetting across a four-stage post-training pipeline and proposes a LoRA expert mixture that lifts AIME26 from 10.21 toward independently trained experts
The work builds ACLArena, a four-stage sequential post-training pipeline spanning math, search, e-commerce, and instruction following; diagnoses forgetting and transfer at both the model level and the token level; systematically compares multi-teacher on-policy distillation (MMOPD), self-distilled fine-tuning (SDFT), and model merging; and proposes Mixture of Low-Rank Experts (MLE), which first consolidates multi-domain trajectories into a shared backbone via SDFT, then freezes the backbone and trains one RL-optimized LoRA expert per stage routed by environment context, improving both in-domain and out-of-domain performance on four reasoning and agentic tasks.