# TRINITY: An Evolved LLM Coordinator

## A Lightweight Coordinator Orchestrating Multiple LLMs via Evolutionary Strategy

**Authors:**  Jinglue Xu, Qi Sun, Peter Schwendeman, Stefan Nielsen, Edoardo Cetin, Yujin Tang  
**Affiliations:** Sakana AI, Japan; University of Michigan, USA; Institute of Science Tokyo, Japan  
**Published:** ICLR 2026 | arXiv: 2512.04695

## Motivation & Key Contributions

Scaling monolithic LLMs yields diminishing returns, and model merging is impractical across incompatible architectures and closed-source APIs. TRINITY adopts a macro-level approach: test-time model composition via coordination, fusing complementary strengths of multiple models without modifying their weights.

- **Lightweight coordination:** Hidden states from an SLM (0.6B) + a tiny head (∼10K params) coordinate diverse LLMs — total learnable parameters under 20K.
- **Efficient training:** sep-CMA-ES outperforms RL, SFT, and random search under tight budget constraints.
- **State-of-the-art:** 86.2% pass@1 on LiveCodeBench. Outperforms all baselines on 8 benchmarks (4 in-distribution, 4 held-out).

## Why Evolutionary Strategy?

Training the coordinator is challenging: each evaluation requires a full multi-turn LLM rollout (expensive), the reward is binary (correct/incorrect), and parameters exhibit weak mutual coupling — each has only a tiny influence on the scalar reward.

- **RL fails:** REINFORCE gradients are low-SNR under binary rewards and weak parameter correlations, yielding unstable learning.
- **SFT is intractable:** Multi-turn label generation scales as O(7 · 3) per question — prohibitively expensive.
- **sep-CMA-ES works:** Derivative-free, diagonal covariance matches the block-separable structure. Improvement grows linearly with iterations (vs. logarithmically for random search). Only ∼1.5K–40K evaluations needed for ∼10K parameters.

## sep-CMA-ES Results

sep-CMA-ES consistently outperforms all alternative training methods:

| Method           | LCB   | MATH  | MMLU  | RLPR  |
|------------------|-------|-------|-------|-------|
| REINFORCE       | 0.253 | 0.459 | 0.500 | 0.266 |
| Random Search    | 0.374 | 0.794 | 0.897 | 0.345 |
| SFT              | 0.592 | 0.786 | 0.906 | 0.360 |
| sep-CMA-ES      | 0.615 | 0.880 | 0.916 | 0.401 |

## Coordination Pipeline

TRINITY processes queries over multiple turns. At each turn, a compact SLM reads the full transcript, and a lightweight head selects an LLM and assigns it one of three roles: Thinker (strategize), Worker (execute), or Verifier (evaluate). The process halts when the Verifier accepts the solution.

## Conclusion

TRINITY demonstrates that a lightweight coordinator (<20K learnable parameters) can orchestrate diverse LLMs to achieve state-of-the-art performance. The results suggest a promising path forward: engineering collaborative AI ecosystems rather than scaling monolithic models.