Introduces a hierarchical latent selection model showing SFT supplies raw module materials in compound traces while RL decomposes them to identify atomic modules and enable recombination for new reasoning configurations.
Proceedings of the AAAI Conference on Artificial Intelligence , volume=
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Latent-GRPO stabilizes reinforcement learning in latent space, delivering 7.86 Pass@1 gains on low-difficulty tasks over latent baselines and 4.27 points over explicit GRPO on high-difficulty tasks with 3-4x shorter reasoning chains.
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From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning
Introduces a hierarchical latent selection model showing SFT supplies raw module materials in compound traces while RL decomposes them to identify atomic modules and enable recombination for new reasoning configurations.
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Latent-GRPO: Group Relative Policy Optimization for Latent Reasoning
Latent-GRPO stabilizes reinforcement learning in latent space, delivering 7.86 Pass@1 gains on low-difficulty tasks over latent baselines and 4.27 points over explicit GRPO on high-difficulty tasks with 3-4x shorter reasoning chains.