6 papers
Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training
Damien Sileo, Valentin Lacombe, Dimitri Kachler
Procedural generators produce useful verifiable reasoning problems at scale, but have received less attention as data for completion-supervised fine-tuning. We introduce Reasoning…
Same Formulas, Different Semantics: Do Language Models Follow Modal Logic Specifications?
Réemi Andrieu, Damien Sileo
Reasoning about necessity and possibility depends on assumptions about accessibility between worlds and about which objects exist at each one. The same inference may therefore hold…
COMPOSITE-Stem
Kyle Waters, Lucas Nuzzi, Tadhg Looram +20
AI agents hold growing promise for accelerating scientific discovery; yet, a lack of frontier evaluations hinders adoption into real workflows. Expert-written benchmarks have prove…
Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training
Valentin Lacombe, Valentin Quesnel, Damien Sileo
Training on verifiable symbolic data is a promising way to expand the reasoning frontier of language models beyond what standard pre-training corpora provide. Yet existing procedur…
Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning
Valentin Lacombe, Valentin Quesnel, Damien Sileo
We introduce Reasoning Core, a new scalable environment for Reinforcement Learning with Verifiable Rewards (RLVR), designed to advance foundational symbolic reasoning in Large Lang…
Saturation-Driven Dataset Generation for LLM Mathematical Reasoning in the TPTP Ecosystem
Valentin Quesnel, Damien Sileo
The scarcity of high-quality, logically sound data is a critical bottleneck for advancing the mathematical reasoning of Large Language Models (LLMs). Our work confronts this challe…