Move blog to root as BLOG.md (per Meta mentor guidance)

Mentor tip: 'Please push Blog.MD into your HF Space' — a separate
markdown file at the root, distinct from the README, makes it obvious
to judges where the writeup lives. Updated README link accordingly.

Also removed training/WhatMakesAGoodSubmission.md (internal hackathon
notes, not for judges).

README.md

@@ -19,7 +19,7 @@ This is **meta-reinforcement learning** for personalization: the agent isn't tra
 
 - **HF Space (the environment)**: https://huggingface.co/spaces/InosLihka/rhythm_env
 - **Training notebook**: [training/RhythmEnv_GRPO_Training.ipynb](training/RhythmEnv_GRPO_Training.ipynb)
-- **Blog post**: [docs/blog_post.md](docs/blog_post.md) — *Teaching an AI to Know You (Without Asking)*
+- **Blog post**: [BLOG.md](BLOG.md) — *Teaching an AI to Know You (Without Asking)*
 - **Headline results**: [docs/results.md](docs/results.md)
 - **Trained model (Algorithm Distillation)**: https://huggingface.co/InosLihka/rhythm-env-meta-trained-sft-v1
 - **Teacher trajectories dataset**: https://huggingface.co/datasets/InosLihka/rhythm-env-teacher-trajectories

training/WhatMakesAGoodSubmission.md

@@ -1,57 +0,0 @@
-# What makes a submission stand out: 
-Pick an ambitious, original problem
-The themes (problems) are deliberately open. Use them as launching pads, not boxes. Judges have seen a lot of chess, snake, tic-tac-toe, and grid-world clones. To score well on innovation,
-you need a genuinely fresh angle. Some questions to ask yourself:
-Does this environment exist to teach an LLM something it currently can’t do well?
-Is the domain underexplored in RL/LLM training?
-Could a researcher write a paper about training on this?
-
-Design a reward signal that actually teaches
-A great environment has a reward function that:
-Provides a rich, informative signal (not just 0/1 at the end)
-Captures something hard to measure in a clever way
-Uses OpenEnv’s Rubric system thoughtfully (composable rubrics > monolithic scoring)
-Is hard to game; an agent that exploits the reward without solving the task should not get high scores
-
-Show real training, end to end
-The bar isn’t “training script exists.” The bar is “training script runs against the environment, the
-agent learns, and you can show it.” Concretely:
-Your training loop should connect to your environment (not a static dataset)
-Train long enough that the curves mean something
-Compare a trained agent vs. a random/untrained baseline; quantitative and/or qualitative
-Include the plots and numbers in your README and writeup
-
-Make your plots readable
-Reviewers spend seconds, not minutes, on each plot. Help them out:
-Label both axes (e.g. “training step” / “episode” on x, “reward” / “loss” on y) and include units where they apply
-Save plots as .png or .jpg and commit them to the repo (don’t leave them only in a Colab cell or a deleted Wandb run) (if you ran via WANBD, please include the link to that specific run of your plots)
-Embed the key plots in your README with a one-line caption explaining what each one shows If you have multiple runs (baseline vs. trained, ablations, etc.), put them on the same axes so the comparison is obvious
-
-
-Tell a story, not an API doc
-Your README, blog, and pitch should answer:
-Problem) what capability gap or interesting domain are you targeting?
-Environment) what does the agent see, do, and get rewarded for?
-Results) what changed after training? Show it.
-Why does it matter) who would care, and why?
-
-A reviewer should be able to read your README in 3~5 minutes and want to try your
-environment.
-
-NOTE: If you have a video, HF post, or anything else interesting, please make sure that it’s linked
-  from your README.
-
-
-Engineer it cleanly (table stakes)
-Engineering quality matters less than ambition, but sloppy work hurts. Make sure you:
-Use OpenEnv’s Environment / MCPEnvironment base classes properly
-Respect the client / server separation (clients should never import server internals)
-Follow the standard Gym-style API (reset, step, state)
-Have a valid openenv.yaml manifest
-Don’t use reserved tool names (reset, step, state, close) for MCP tools
-
-Final Note
-Judges are looking for environments that push the frontier of what we can train LLMs to do. Be
-ambitious. Pick a problem you find genuinely interesting; that almost always produces better
-work than chasing what you think judges want. Good luck.
-