Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This blogpost is a very approachable and insightful introduction to the idea of random network distillation (RND).

I enjoyed the didactic nature of the explanation through the post. The notion of a state-based method, a prediction-based method, and the discussion on prediction errors were interesting to read. (I had hoped to see relevant citations for the "sources of prediction errors" discussion). The discussion surrounding reward leaking and the intrinsic-extrinsic contrast is interesting and well-presented with visual aids.

That said, I felt that the actual "blog post" itself could have used a number of common web features that would have made the reading experience more interactive. Some concrete examples which should be easy to implement are:
1. Hyperlinking all approaches, opposed to snipping latex tables (e.g., the Atari environments list)
2. It would be useful to replace all of the LaTeX snippets (i.e., screengrab of the abstract, screengrabs of tables and theorems, etc.) with actual HTML-compatible versions. Not doing so also creates issues of accessibility which wouldn't otherwise have persisted.
3. I had also hoped that the plots would actually be javascript / similar entities (e.g., plotly / tensorboard-like embeds) that would allow dashboard-style visualization and exploration.

Overall, the "content" of the blog post is well-written, caters to a broad audience, and provides a few more insights (and a different exposition style) compared to the 2019 ICLR paper where the technique was first published. However, in doing so, the post does not really end up using the features of a blog post that truly make it a "blog" and rather rely on what appear to be a number of screengrabs from the paper. I would strongly recommend addressing these issues before the post can be accepted/published.
review_title: Too many screengrabs

Meta-review: We felt that the blog post employed too many screenshots from the original paper. There was not enough added value.
Decision: Reject