Papers by Chuyu Liu

2 papers
RATIONALYST: Pre-training Process-Supervision for Improving Reasoning (2025.acl-long)

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Challenge: RATIONALYST is a model for process-supervision of reasoning based on pretraining on rationale annotations extracted from unlabeled data.
Approach: They propose a model for process-supervision of reasoning based on pre-training on rationale annotations extracted from unlabeled data.
Outcome: RATIONALYST improves reasoning accuracy by 3.9% on representative reasoning benchmarks.
Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement (2025.acl-long)

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Challenge: Existing high-quality human-annotated SFT data is a bottleneck for Large Language Models (LLMs).
Approach: They propose a two-stage synthetic data generation framework that incorporates World Knowledge Trees and Self-Reflection Refinement to produce high-quality SFT data at scale.
Outcome: The proposed model fine-tuned on 20K condor-generated samples achieves superior performance compared to instruct model trained with RLHF.

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