reviews: 
Comments: This paper proposed a new structure-based method for plasticity and stability tradeoff  in continual RL. Specifically, the authors proposed (1) a sub-network co-allocation method which enhances meta sparse prompting by task-specific sparse prompting, (2) a finegrained masking method which only freeze exact parameters trained in previous tasks, and (3) a periodic resetting strategy for dormant neurons. However, this work is largely built upon previous works, which limits its technical contributions. And the reported experimental results are somewhat misleading. Thus, I lean towards reject at current stage.
Strengths: 1. The authors proposed to separate sub-network sparse prompting into global and task-specific levels, which seems effective in their ablation study. 

2. The introduction of parameter-level masking and dormant neuron resetting techniques is beneficial for mitigating plasticity loss and keeping the learning capability of networks. 

3. Combining the proposed techniques, the overall framework achieves higher computation efficiency.
Weaknesses: 1. This paper largely follows the technical parts of [1][2], which limits their own contribution. In detail,

1.1. The authors claim a crucial contribution of introducing task-level sparse prompting, which has also been proposed in [1], where the task dictionary is obtained through a dictionary learning algorithm using previous tasks' optimized prompts and their embedding. Here the authors sampled the task dictionary from N(0, 1) which shares the same distribution of global dictionary. I don't see the motivation of this choice. Could the authors elaborate more on this part and also detail the difference between [1] and their method?

1.2. In 4.2, since periodic dormant neuron resetting is well established in [2] for continual RL, it is inappropriate to claim this as a key contribution of their framework. In addition, the use of "exploration" is arguably misleading, which has various meanings in different contexts. I would suggest the authors use the specific term "structural exploration" throughout the paper for better clarification. 

2. There are many model choices made without sufficient justification. In addtion to 1.1, the proposed "sensitivity dormant scores" also lacks clear motivation. What is the advantage of this metric than previously defined dormant scores? And ablation study is necessary for justifying such choices. 

3. The experiments results seem misleading.

3.1. Throughout the experiments, their reproduced results of CoTASP has a huge gap with the reported ones in CoTASP's paper (e.g. for P: 0.73 v.s. 0.92 in CW10 and 0.74 v.s. 0.88 in CW20), which has no essential difference with SSDE (CW10) or even better (CW20). And the generation performance of SSDE is not reported for a fair comparison with previous methods. 

3.2. In addition, the results are quite misleading given the reported P in CoTASP. In Table 4, the average success of SSDE w/o Dormant is 0.85 while that of CoTASP which also don't use Dormant neuron resetting achieves 0.92. Does this indicate that the proposed co-allocation strategy is inferior to the allocation method in [1] which also use sparse prompting? 

4. The overall frameworks contain a lot of hyperparameters, such as the sparsity controlling parameters in Equation (3-4), the trade-off paramter in Equation (6), and the dormant threshold in Definition 4.1, which make the framework less practical. In addition, no ablation results are provided to show the robustness of the system to these hyperparameters. 

[1] Yang, Yijun, et al. "Continual task allocation in meta-policy network via sparse prompting." International Conference on Machine Learning. PMLR, 2023.

[2] Sokar, Ghada, et al. "The dormant neuron phenomenon in deep reinforcement learning." International Conference on Machine Learning. PMLR, 2023.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2