Refresh ARC datasets section

README.md

@@ -37,17 +37,15 @@ 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).
+📚 **ARC-Related Datasets & Frameworks**
+- [RE-ARC](https://github.com/michaelhodel/re-arc) — procedurally generates examples for the 400 ARC training tasks (we also include RE-ARC eval + ARC 1.5).
+- [ConceptARC](https://github.com/victorvikram/ConceptARC)
+- [1D-ARC](https://khalil-research.github.io/LLM4ARC/)
+- ARC_gym, Sort-of-ARC
+- Andreas Koepf’s generator suites (includes RE-ARC-style grids, code generation targets, and solution graphs).
+- Jack Cole’s custom generators covering ~70 tasks plus larger concept sets (cellular automata, math-derived boards, etc.).
 
-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.
+Several auxiliary datasets predict task metadata (graphs, heuristics, explanations) rather than final boards; they are part of the broader instruction mixture this model saw during pretraining.
 
 ## ARC Data Formatting