Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and If you ran experiments... (a) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? Please refer to both main text and appendix for experiment details. Did you report error bars (e.g., with respect to the random seed after running experiments multiple All adaptation experiments in Procgen and RLBench are run for 3 seeds. Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal As stated in section 2, we use RTX A5000 GPUs each with 24GB memory. C2F-ARM algorithm and training framework are built based on the original author's implementation Did you mention the license of the assets?

Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

The heads are then concatenated to form the LoRA adapter.

The circles and crosses represent the object and goal positions, respectively.

Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster.