R1-Searcher uses two-stage outcome-based RL to train LLMs to invoke external search systems for better reasoning without process rewards or distillation.
Reinforce++: A simple and efficient approach for aligning large language models
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2025 2verdicts
UNVERDICTED 2roles
method 1polarities
use method 1representative citing papers
Rule-based RL on 5K logic puzzles induces advanced reasoning in a 7B model that transfers to AIME and AMC.
citing papers explorer
-
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
R1-Searcher uses two-stage outcome-based RL to train LLMs to invoke external search systems for better reasoning without process rewards or distillation.
-
Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning
Rule-based RL on 5K logic puzzles induces advanced reasoning in a 7B model that transfers to AIME and AMC.