ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning | Paper Citation Record

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This paper cites Advances in Neural Information Processing Systems , volume=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 1

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This paper cites DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 2

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This paper cites Pan and H.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Pan and H

Reference 3

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This paper cites CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 4

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This paper cites arXiv preprint arXiv:2601.18533 , year=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning arXiv preprint arXiv:2601.18533 , year=

Reference 5

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This paper cites Hansen and Duo Peng and Yuhui Zhang and Alejandro Lozano and Min Woo Sun and Emma Lundberg and Serena Yeung-Levy , year=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Hansen and Duo Peng and Yuhui Zhang and Alejandro Lozano and Min Woo Sun and Emma Lundberg and Serena Yeung-Levy , year=

Reference 6

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

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This paper cites Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation

Reference 8

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This paper cites Save the good prefix: Precise error penalization via process-supervised rl to enhance llm reasoning.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Save the good prefix: Precise error penalization via process-supervised rl to enhance llm reasoning

Reference 9

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This paper cites Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A

Reference 10

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This paper cites Advances in Neural Information Processing Systems , volume=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Advances in Neural Information Processing Systems , volume=

Reference 11

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This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 12

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This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 13

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This paper cites Proceedings of the 2023 conference on empirical methods in natural language processing , pages=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Proceedings of the 2023 conference on empirical methods in natural language processing , pages=

Reference 14

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This paper cites Can AI Assistants Know What They Don't Know?.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Can AI Assistants Know What They Don't Know?

Reference 15

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This paper cites Advances in Neural Information Processing Systems , volume=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Advances in Neural Information Processing Systems , volume=

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This paper cites Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience

Reference 17

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This paper cites Advances in neural information processing systems , volume=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Advances in neural information processing systems , volume=

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning International Conference on Learning Representations , volume=

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Findings of the Association for Computational Linguistics: ACL 2024 , year=

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=

Reference 21

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning arXiv preprint arXiv:2503.02623 (2025)

Reference 22

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 23

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 25

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models

Reference 26

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This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 27

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Deep Think with Confidence

Reference 28

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Harder is better: Boost- ing mathematical reasoning via difficulty-aware grpo and multi-aspect question reformulation

Reference 29

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Understanding R1-Zero-Like Training: A Critical Perspective

Reference 30

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Advances in Neural Information Processing Systems , volume=

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Group Sequence Policy Optimization

Reference 32

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Soft Adaptive Policy Optimization

Reference 33

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization

Reference 34

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 35

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 36

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Proceedings of the Twentieth European Conference on Computer Systems , pages=

Reference 37

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Qwen3 Technical Report

Reference 38

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This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 39

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 40

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Measuring Massive Multitask Language Understanding

Reference 41

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Advances in neural information processing systems , volume=

Reference 42

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 43

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 44

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ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning arXiv preprint arXiv:2503.17736 , year=

Reference 45

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This paper cites VCR-Bench: A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning VCR-Bench: A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning

Reference 46

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This paper cites Vision-deepresearch benchmark: Rethinking visual and textual search for multimodal large language models.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning Vision-deepresearch benchmark: Rethinking visual and textual search for multimodal large language models

Reference 47

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This paper cites arXiv preprint arXiv:2510.01304 , year=.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning arXiv preprint arXiv:2510.01304 , year=

Reference 48

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This paper cites VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation

Reference 49

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Coverage vector

Reference resolution

Outbound references

Pith citing papers