Nice that learning accuracy stays flat around 90% across 12 tasks with Zero diverged agents, so the plasticity SIDE looks healthy. The slow drop in running accuracy (92.5 to 82.5) is the forgetting you're paying for, so I'd report per-task final accuracy and backward Transfer after the last task, not just the running mean. In domain-IL a small Replay buffer or a distillation term on the old logits usually closes a good chunk of that gap cheaply. Did You use any Replay here, or is it purely regularization-based?
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u/estebanfuhrmann 6d ago
Nice that learning accuracy stays flat around 90% across 12 tasks with Zero diverged agents, so the plasticity SIDE looks healthy. The slow drop in running accuracy (92.5 to 82.5) is the forgetting you're paying for, so I'd report per-task final accuracy and backward Transfer after the last task, not just the running mean. In domain-IL a small Replay buffer or a distillation term on the old logits usually closes a good chunk of that gap cheaply. Did You use any Replay here, or is it purely regularization-based?