Merge remote-tracking branch 'upstream/main' into temp-fix-sft

Author: joecummings

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -8,47 +8,17 @@ body:
       #### Before submitting a feature request, please search through [existing issues](https://github.com/meta-pytorch/forge/issues?q=is%3Aissue+sort%3Acreated-desc+) to see if something similar has already been proposed.
 - type: textarea
   attributes:
-    label: 🎯 Context/Motivation
+    label: Context/Motivation
     description: |
-      Describe the problem you're trying to solve or the use case for this feature.
-      What is the motivation behind this request? Why would this feature be valuable?
-    placeholder: |
-      Example: "When training large models, I often need to..."
+      Describe the problem you're trying to solve or the use case for this feature. Include any relevant links and context.
   validations:
     required: true
 - type: textarea
   attributes:
-    label: 💡 What does this look like in pseudo-code?
+    label: Pseudo-code + acceptance criteria [Optional]
     description: |
-      Provide a rough sketch of what the API or implementation might look like.
-      This helps us understand your vision for how the feature would work.
-    placeholder: |
-      ```python
-      # Example pseudo-code showing how you envision using this feature
-      model = MyModel()
-      result = model.new_feature(...)
-      ```
-  validations:
-    required: false
-- type: textarea
-  attributes:
-    label: ✅ Acceptance Criteria
-    description: |
-      What would need to be true for this feature to be considered complete?
-      List the key requirements or behaviors expected.
-    placeholder: |
-      - [ ] The feature should support X
-      - [ ] It should handle edge case Y
-      - [ ] Documentation is updated
-  validations:
-    required: true
-- type: textarea
-  attributes:
-    label: 📝 Additional Context
-    description: |
-      Add any other context, screenshots, examples, or references that would help explain the feature request.
-    placeholder: |
-      Links to similar implementations, research papers, or other relevant information.
+      Provide a rough sketch of what the API or implementation might look like. This helps us understand your vision for how the feature would work.
+      Also, if possible, include what would need to be true for this feature to be considered complete.
   validations:
     required: false
 - type: markdown

README.md

@@ -6,7 +6,7 @@
 [![Discord](https://img.shields.io/badge/Discord-OpenEnv-7289da?style=flat&logo=discord&logoColor=white)](https://discord.gg/YsTYBh6PD9)
 
 ## Overview
-The primary purpose of the torchforge ecosystem is to delineate infra concerns from model concerns thereby making RL experimentation easier. torchforge delivers this by providing clear RL abstractions and one scalable implementation of these abstractions. When you need fine-grained control over placement, fault handling/redirecting training loads during a run, or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.
+The primary purpose of the torchforge ecosystem is to separate infra concerns from model concerns thereby making RL experimentation easier. torchforge delivers this by providing clear RL abstractions and one scalable implementation of these abstractions. When you need fine-grained control over placement, fault handling/redirecting training loads during a run, or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.
 
 Key features:
 - Usability for rapid research (isolating the RL loop from infrastructure)