Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The authors propose the Regularized Offline GFlowNets algorithm to solve the problem of OOD generalisation in GFlowNets. They trie to achieve this by regularizing the distribution distance of policy and offline datasets while minimizing the loss. To back their proposed algorithm, they show experimental results that their model works better than vanilla GFLowNets
strengths_and_weaknesses: Strength:
* The paper proposes a novel algorithm to solve the problem of OOD generalisation in GFlowNets and demonstrate effectiveness of their algorithm with ablation against vanilla GFlowNets.

Weakness:
* The paper does not talk about the experimental setup and dataset used to perform the comparison ablation with GFlownet.
* Citations should be in parentheses as they break up the sentences.
suggested_changes: Please look over the weakness above.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The paper proposes adding a dataset constraint to the regular GFlowNet training objective in order to handle offline training data. The constraint is well-motivated and follows standard the offline RL literature. The resultant objective shows an improvement on a toy example.
strengths_and_weaknesses: Strengths
- Clear presentation and well-motivated addition, resembling the BEAR algorithm in offline RL
- Promising direction for future work, method should scale to larger datasets
suggested_changes: - Citations should be in parentheses to not break up sentences. E.g. using \citep{...}
- Presentation of Equation 1 is slightly confusing as it is unclear that is constrained (only explained on the next page).

Minor
- ‘To be’ -> ‘to be in’ (page 2 top)
review_title: null

Meta-review: Please add your URM statement.
Decision: Invite to present