Venue: NeurIPS 2022 Workshop Offline_RL

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The paper presents a novel method for retaining offline-RL agents conditioned on natural language instructions. Their main novelties stem from techniques which augment their human provided language labels by using an LM to summarize longer sequences of actions, and by explicitly chaining instructions from different trajectories together. These techniques enable learning from many more instructions than are in the original dataset. They do a thorough evaluation showing their method to significantly outperform baselines in settings with long sequences of instructions. They also show through ablations that their main novelties are critical for their good performance. Overall I think the paper is well written and executed, and deserves acceptance into the conference.
review_title: A good paper demonstrating a novel set of techniques for pertaining language-conditioned agents with Offline-RL

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper proposes a method called SPRINT, which enables scalable agent pre-training with offline instruction-conditioned RL using natural language interactions. SPRINT introduces two approaches for increasing the diversity of the pre-training instructions, namely language-model-based interaction aggregation and cross-entropy chasing. 
Experiments on the ALFRED household task benchmark demonstrate the efficient pre-training of SPRINT compared to previous methods.

Even though the paper says that it studies the offline RL problem, there seem to be no offline-specific technical challenges addressed in the paper. Therefore, due to insufficient technical relevancy to the workshop, I'd suggest a weak rejection. 
review_title: Review of SPRINT: Scalable Semantic Policy Pre-training via Language Instruction Relabeling 

Decision: Accept