Update README.md

README.md

@@ -37,6 +37,18 @@ This checkpoint is the primary CodeT5-based solver we used for the MindsAI @ Tuf
 - **Decoder-only pruning**: the original decoder depth (24) was reduced to 16 layers after experiments showed encoder pruning harmed sample efficiency, while decoder pruning could be recovered through extended training.
 - **Long-run TPU training**: training spanned roughly two years on a V4-64 TPU, made possible by Google’s TPU Research Cloud program.
 
+📚 ARC-Related Datasets & Frameworks
+RE-ARC Link: https://github.com/michaelhodel/re-arc
+Note: This is the repository from Michael Hodel, which procedurally generates examples for the 400 ARC training tasks.  We also include RE-ARC eval and ARC 1.5 (also by Michael Hodel).
+ConceptARC Link: https://github.com/victorvikram/ConceptARC
+1D-ARC (likely "ID ARC") Link: https://khalil-research.github.io/LLM4ARC/
+ARC_gym
+Sort-of-ARC
+Andreas Koepf - Generated many tasks based upon the RE-ARC methodology using various foundation models.  Additionally generated from a generator Andreas wrote based on the icecuber solution.  It also includes extra tasks like predicting the solution graph.
+Jack Cole - Wrote generators for 60-80 tasks.  Many were inspired by ARC items.  Others were large concept datasets (cellular automata, math equation derived boards).
+
+There is a large amount of ARC-related tasks that are not solving for the board (like generating code, predicting various parameters or features related to the task). There are other non-ARC related tasks.
+
 ## ARC Data Formatting
 
 - ARC tasks ship as JSON where each `task_id` contains `train` pairs and `test` inputs; every grid is a rectangular list of lists with integers `0-9`. Dimensions follow the original 1×1–30×30 spec, though the evaluator accepts up to 50×50.