ARC-AGI-2: 100M Parameter Model Trained on a Single RTX 4090
Test the 100M parameter model on a single RTX 4090 to gauge performance and explore recursive reasoning.
Experiment with recursive reasoning on consumer GPUs to evaluate architecture efficiency.
Summary
A research team has built a 100‑million‑parameter model for the ARC‑AGI‑2 competition using only a single RTX 4090 GPU. After 14 days of training, the model achieved 11.67 % on the public leaderboard, while a local evaluation reached 36 % before threshold adjustments. Recursive loops in the architecture cause heavy computation, leading to null outputs for nearly half the puzzles when thresholds are set too high to avoid submission timeouts. The team expects a 20 % score once time‑management logic is refined, and projects that an additional 3–5 weeks of training could deliver a groundbreaking leaderboard position. This work demonstrates that small models employing biological memory can outperform compute‑heavy approaches, highlighting an architecture war rather than a pure compute war.
Key changes
- Trained a 100M parameter model on a single RTX 4090.
- Reached 11.67 % on the public ARC‑AGI‑2 leaderboard after 14 days of training.
- Local evaluation achieved 36 % accuracy before threshold adjustments.
- Recursive loops cause heavy computation, leading to null outputs for nearly half the puzzles.
- Thresholds set too high to avoid submission timeout, reducing score.
- Expected 20 % score after refining time‑management logic.
- Projected 3–5 weeks of additional training could yield a groundbreaking leaderboard position.
- Demonstrates that small models with biological memory can outperform compute‑heavy approaches.