Debugging the "Tiny Recursive Model" — My 3-Day Deep Dive into Samsung’s AI Maze
As a 3rd-year CSE student, I’m always chasing the next challenge — the kind that looks simple at first and ends up teaching you more than any class ever could. That’s exactly what happened when I decided to set up and run Samsung’s “Tiny Recursive Model (TRM)". I thought it would be a quick weekend experiment. I was so wrong. What followed was a three-day odyssey through broken dependencies, C++ compiler issues, CUDA configs, and critical flaws buried deep in the source code.
But this isn’t a story about achieving some perfect accuracy score. It’s about tearing apart a broken system, figuring out why it doesn’t work, and coming out smarter on the other side.
🧩 Day 1 — The Environment Fails
My first attempt to run the classic pip install -r requirements.txt on Windows failed instantly.
Error: ERROR: No matching distribution found for triton
Diagnosis: Triton — a key dependency — is Linux-only. The whole project was never meant to run on Windows.
Fix: Installed WSL (Windows Subsystem for Linux), set up Ubuntu, and started completely fresh. That was my first lesson: sometimes the environment is the first boss fight.
⚙️ Day 2 — The Compilation Hell
With Linux up and running, I was feeling confident… until I wasn’t.
Error: ModuleNotFoundError: No module named 'adam_atan2_backend'
Diagnosis: This wasn’t Python’s fault — it was a C++ compilation issue. The adam-atan2 library couldn’t find a C++ compiler.
Fix: Installed the build toolchain using sudo apt install build-essential. Of course, that fix just unlocked the next problem: OSError: CUDA_HOME environment variable is not set.
Now the compiler was fine, but it couldn’t locate nvcc, the CUDA compiler. So I went all in:
That small success felt like a victory after hours of trial and error.
⏱️ Day 3 — The "1300-Year" Bottleneck
With everything running, I began training on the Sudoku dataset. I fixed an initial out-of-memory error by simplifying the model’s architecture... and then hit a wall.
The Problem: The training was “running,” but it was impossibly slow — ~14 seconds per iteration.
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Diagnosis: The bottleneck wasn’t the GPU; it was CPU-bound file I/O inside WSL. The GPU sat idle while the CPU handled data loading.
At this rate, training for 50,000 epochs would take over 1,300 years. Dead end.
🔁 Day 4 — The Evaluation Pivot
Training was a lost cause, so I pivoted to evaluation.
I tried to evaluate the Sudoku model (sourced manually, since Samsung’s link was broken). It instantly failed — No evaluator found. The evaluators/sudoku.py script was missing entirely.
So I turned to the ARC Prize model and its arc.py evaluator.
🛠️ The Final, Double-Flaw Diagnosis
Running the ARC model (which I successfully sourced from Hugging Face) exposed two deep flaws:
The Verdict: The pre-trained ARC model is fundamentally too heavy for consumer hardware. Even for simple inference (batch size 1), the core tensor calculations exceed 6GB VRAM. It wasn't a code error anymore; it was a physical hardware barrier.
🏁 The Real Win
I didn’t walk away with an accuracy score — I walked away with understanding.
The repository was incomplete, referencing missing model files and broken evaluators, and the TRM itself was too memory-heavy for consumer GPUs. Instead of giving up, I mapped every failure to its cause, fixed what I could, and documented what couldn’t be fixed.
My final conclusion:
The TRM repository is fundamentally incomplete — missing pretrained models, missing evaluators, and incapable of running on standard hardware without extensive patching.
That, to me, was the real victory — not running the code, but understanding its limits and knowing when to pivot.
📂 You can find this project on my profile under “Projects.”