## Can Neural Networks Learn Implicit Logic from Physical Reasoning?
Aaron Traylor, Roman Feiman, Ellie Pavlick
Keywords: 
ICLR/2023/Proceedings/10947 - Can Neural Networks Learn Implicit Logic from Physical Reasoning?.pdf
Project URL: https://openreview.net/attachment?id=HVoJCRLByVk&name=supplementary_material

### Implementation
_Given the documentation shared by the authors on a new method, how much effort would it be to re-implement the method from scratch?_

[10]

Authors do not provide their implementation. 

### Data
_Given the data description in the documentation, how much effort would it take to either: Find the same data set the authors used, or a similar data set and defend the comparability, or acquire one from scratch?_

[4]

(0/2)

The authors use Physical Reasoning + Two-Cup Ablations data. The tasks are described in section 3. Code not provided.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for obtaining the reported results, and compare them against their computation budget?_

[2]

The authors discuss HP values in appendix F, discuss evaluated grids and values. Structured overview not given.

### Experimental Procedure
_Given the setup of experiments reported in the work, how difficult is it to set up a new experiment with the same procedure, similar to those presented in the original work?_

[2]

Data split detailed in 4.1. and measure accuracy and later present averaged results over 5 seeds but do not detail the variation.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying solely on the available documentation?_

[4]

-
