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Found 8 papers, 5 papers with code
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
In this paper, we propose R$^3$: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the benefits of process supervision for large language models.
ReFT: Reasoning with Reinforced Fine-Tuning
ReFT first warmups the model with SFT, and then employs on-line reinforcement learning, specifically the PPO algorithm in this paper, to further fine-tune the model, where an abundance of reasoning paths are automatically sampled given the question and the rewards are naturally derived from the ground-truth answers.
Design of Chain-of-Thought in Math Problem Solving
We also find that Python is a better choice of language than Wolfram for program CoTs.
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning
Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR).
NAIL: A Challenging Benchmark for Na\"ive Logical Reasoning
Logical reasoning over natural text is an important capability towards human level intelligence.
Probabilistic Graph Reasoning for Natural Proof Generation
In this paper, we propose PRobr, a novel approach for joint answer prediction and proof generation.
LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification
The final claim verification is based on all latent variables.
A State-transition Framework to Answer Complex Questions over Knowledge Base
Although natural language question answering over knowledge graphs have been studied in the literature, existing methods have some limitations in answering complex questions.
