MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier
Abstract
MOOSE-Star framework enables efficient training and inference for generative reasoning by addressing combinatorial complexity through decomposed subtasks, hierarchical search, and bounded composition.
While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, leaving the direct modeling of the generative reasoning process, P(hypothesis|background) (P(h|b)), unexplored. We demonstrate that directly training P(h|b) is mathematically intractable due to the combinatorial complexity (O(N^k)) inherent in retrieving and composing inspirations from a vast knowledge base. To break this barrier, we introduce MOOSE-Star, a unified framework enabling tractable training and scalable inference. In the best case, MOOSE-Star reduces complexity from exponential to logarithmic (O(log N)) by (1) training on decomposed subtasks derived from the probabilistic equation of discovery, (2) employing motivation-guided hierarchical search to enable logarithmic retrieval and prune irrelevant subspaces, and (3) utilizing bounded composition for robustness against retrieval noise. To facilitate this, we release TOMATO-Star, a dataset of 108,717 decomposed papers (38,400 GPU hours) for training. Furthermore, we show that while brute-force sampling hits a ''complexity wall,'' MOOSE-Star exhibits continuous test-time scaling.
Community
Most current LLMs for scientific discovery rely on inference-time prompting or external feedback for training. But how can we directly train an LLM to generate scientific hypotheses from a research background, i.e., P(h|b)?
In this work, we theoretically demonstrate that directly training this generative process is computationally intractable due to the O(N^k) combinatorial complexity of retrieving and composing scientific inspirations.
To break this barrier, we introduce MOOSE-Star, a unified framework that reduces this complexity to O(log N) via Motivation-Guided Hierarchical Search and Bounded Composition.
š„ Key Highlights & Open-Source Contributions:
- Tractable & Scalable Training: The first framework to enable scalable training for the direct generation of scientific discoveries.
- Superior Test-Time Scaling: While brute-force unguided sampling hits a "complexity wall" on multi-step problems, MOOSE-Star exhibits continuous test-time scaling for discovery.
- TOMATO-Star Dataset š : We are fully open-sourcing our data engine! It contains 108,717 open-access papers rigorously decomposed into (Background, Hypothesis, Inspirations) tuples, which cost ~38,400 A800 GPU hours to build.
- Models & Code: We have released our fine-tuned R1-Distilled-7B models (IR and HC modules) and the complete training/inference pipeline.
We hope this opens up new tractable pathways for the AI4Science community!
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