reviews: 
Comments: This paper proposes a structure-based continual reinforcement learning algorithm. To address the stability-plasticity dilemma in the previous subnetwork allocation approaches, the authors propose a fine-grained allocation strategy with two key designs: (1) The parameter space is decomposed into forward-transfer and task-specific components, which are co-allocated by sparse coding to enhance plasticity. (2) The dormant neurons are periodically reactivated to improve exploration and rapid adaptation in new tasks. The proposed method is validated on the Continual World benchmark and shows significant simprovement in performance.
Strengths: * The paper is generally well written and very informative.
* The technical contributions (fine-grained subnetwork allocation and dormant neuron re-activation) seem solid, both effectively addressing the stability-plasticity dilemma in continual reinforcement learning.
* The experimental results on the Continual World benchmark show great improvements over the existing baselines.
Weaknesses: * The second contribution (dormant neuron re-activation) could be validated more carefully. There are already several works dealing with dormant neurons (e.g. [1, 2]), so a comparison with the existing literature in terms of method differences and experimental results would help to justify the contribution of this paper. Also, since the authors propose a new criterion for dormant neurons, it should be investigated how it overlaps with the original definition in [1], is it possible that it induces false positives and thus destabilizes the learning process? 
* The proposed method utilizes task embeddings from a pre-trained BERT, which limits its applicability in broader scenarios involving implicit knowledge that cannot be verbalized. For example, while BERT embeddings work fine in the Continual World benchmark involving manipulation tasks, they may be difficult to transfer to the Brax scenarios used in [3, 4] involving locomotion tasks. It would be interesting to see further experiments or discussions on this issue.
* Regarding efficiency, the authors only discussed their allocation time, but there is no mention of total training time and model size. These are more representative metrics for evaluating the efficiency of continual reinforcement learning, and should be reported in detail in the paper. Also, it would be better if the authors could include an intuitive figure of the performance-size or performance-training time tradeoff, perhaps like Figure 1 in [3] and Figure 4 in [4].
* There is a lack of sensitivity analysis for several hyperparameters, including $\beta$ in Equation (6), $\Delta$ in Equation (8) and $\tau$ for thresholding. The paper determined these hyperparameters by grid search, but I am curious if their choice is critical to the final performance and difficult to choose in practice. The authors could present a sensitivity analysis of these hyperparameters to address my concern, and additional studies of their generalizability across different types of tasks would be appreciated.

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[1] Sokar, et al. The dormant neuron phenomenon in deep reinforcement learning. ICML 2023.

[2] Dohare, et al. Loss of plasticity in deep continual learning. Nature 2024.

[3] Gaya, et al. Building a subspace of policies for scalable continual learning. ICLR 2023.

[4] Sun, et al. Rewiring neurons in non-stationary environments. NeurIPS 2023.
Recommendation: 8
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 2