Tropical reinforcement learning algorithm tropic improves compositional reasoning
Tropical reinforcement learning (TROPIC) is a new training algorithm that replaces the traditional sum of probabilities with a maximum operation, creating a tropical semiring. The method records the log‑probability of the most likely verified solution and its explicit path, allowing the model to combine successful prefixes and suffixes from separate rollouts.
Key points
- TROPIC replaces probability sum with a maximum, forming a tropical semiring
- It records the log‑probability of the best verified solution and its path
- On four tasks, TROPIC beats on‑policy baselines by up to 16 percentage points
On four deterministic, resettable tasks—Sokoban, Countdown, FrozenLake, and WebShop—TROPIC outperforms the strongest on‑policy baselines by up to 16 percentage points. The authors argue that this algebraic change addresses the shortcomings of expected return for compositional reasoning, where solutions must be assembled from multiple reasoning steps.
The paper presents TROPIC as a proof of concept for improving compositional reasoning in large language models, but it does not report any commercial deployment or funding. The work remains a research contribution rather than a product release.
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