Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought
Fuente:
arXiv
Saved in:
| Main Authors: | Xie, Zixuan, Liu, Xinyu, Chandra, Rohan, Zhang, Shangtong |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Provable Emergence of In-Context Reinforcement Learning
by: Wang, Jiuqi, et al.
Published: (2025)
by: Wang, Jiuqi, et al.
Published: (2025)
Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning
by: Xie, Zixuan, et al.
Published: (2026)
by: Xie, Zixuan, et al.
Published: (2026)
Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift
by: Liu, Xinyu, et al.
Published: (2026)
by: Liu, Xinyu, et al.
Published: (2026)
Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
by: Xie, Zixuan, et al.
Published: (2025)
by: Xie, Zixuan, et al.
Published: (2025)
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)
by: Qian, Xiaochi, et al.
Published: (2024)
Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set
by: Liu, Xinyu, et al.
Published: (2025)
by: Liu, Xinyu, et al.
Published: (2025)
Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning
by: Liu, Xinyu, et al.
Published: (2025)
by: Liu, Xinyu, et al.
Published: (2025)
A Survey of In-Context Reinforcement Learning
by: Moeini, Amir, et al.
Published: (2025)
by: Moeini, Amir, et al.
Published: (2025)
Safe In-Context Reinforcement Learning
by: Moeini, Amir, et al.
Published: (2025)
by: Moeini, Amir, et al.
Published: (2025)
Group Fairness in Multi-Task Reinforcement Learning
by: Song, Kefan, et al.
Published: (2025)
by: Song, Kefan, et al.
Published: (2025)
MathlibPR: Pull Request Merge-Readiness Benchmark for Formal Mathematical Libraries
by: Xie, Zixuan, et al.
Published: (2026)
by: Xie, Zixuan, et al.
Published: (2026)
Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL
by: Khurram, Aleesha, et al.
Published: (2025)
by: Khurram, Aleesha, et al.
Published: (2025)
Experience Replay Addresses Loss of Plasticity in Continual Learning
by: Wang, Jiuqi, et al.
Published: (2025)
by: Wang, Jiuqi, et al.
Published: (2025)
Towards Formalizing Reinforcement Learning Theory
by: Zhang, Shangtong
Published: (2025)
by: Zhang, Shangtong
Published: (2025)
Reward Is Enough: LLMs Are In-Context Reinforcement Learners
by: Song, Kefan, et al.
Published: (2025)
by: Song, Kefan, et al.
Published: (2025)
Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning
by: Mahadevan, Vagul, et al.
Published: (2026)
by: Mahadevan, Vagul, et al.
Published: (2026)
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
by: Wang, Jiuqi, et al.
Published: (2024)
by: Wang, Jiuqi, et al.
Published: (2024)
Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
by: Wang, Jiuqi, et al.
Published: (2024)
by: Wang, Jiuqi, et al.
Published: (2024)
Doubly Optimal Policy Evaluation for Reinforcement Learning
by: Liu, Shuze Daniel, et al.
Published: (2024)
by: Liu, Shuze Daniel, et al.
Published: (2024)
Efficient Multi-Policy Evaluation for Reinforcement Learning
by: Liu, Shuze Daniel, et al.
Published: (2024)
by: Liu, Shuze Daniel, et al.
Published: (2024)
Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning
by: Chen, Claire, et al.
Published: (2024)
by: Chen, Claire, et al.
Published: (2024)
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
by: Huang, Sili, et al.
Published: (2024)
by: Huang, Sili, et al.
Published: (2024)
Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
by: Blaser, Ethan, et al.
Published: (2026)
by: Blaser, Ethan, et al.
Published: (2026)
The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Liu, Shuze Daniel, et al.
Published: (2024)
by: Liu, Shuze Daniel, et al.
Published: (2024)
Counterfactual Explanations for Continuous Action Reinforcement Learning
by: Dong, Shuyang, et al.
Published: (2025)
by: Dong, Shuyang, et al.
Published: (2025)
CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening
by: Kulkarni, Amar, et al.
Published: (2024)
by: Kulkarni, Amar, et al.
Published: (2024)
Transformers Provably Learn to Internalize Chain-of-Thought
by: Huang, Yixiao, et al.
Published: (2026)
by: Huang, Yixiao, et al.
Published: (2026)
Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought
by: Huang, Jianhao, et al.
Published: (2025)
by: Huang, Jianhao, et al.
Published: (2025)
Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
by: Song, Kefan, et al.
Published: (2025)
by: Song, Kefan, et al.
Published: (2025)
On the Divergence of Differential Temporal Difference Learning without Local Clocks
by: Antrobius, David, et al.
Published: (2026)
by: Antrobius, David, et al.
Published: (2026)
Revisiting a Design Choice in Gradient Temporal Difference Learning
by: Qian, Xiaochi, et al.
Published: (2023)
by: Qian, Xiaochi, et al.
Published: (2023)
Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design
by: Liu, Shuze, et al.
Published: (2023)
by: Liu, Shuze, et al.
Published: (2023)
Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning
by: Parker, Felix, et al.
Published: (2025)
by: Parker, Felix, et al.
Published: (2025)
Emergence of Superposition: Unveiling the Training Dynamics of Chain of Continuous Thought
by: Zhu, Hanlin, et al.
Published: (2025)
by: Zhu, Hanlin, et al.
Published: (2025)
Emergence of In-Context Reinforcement Learning from Noise Distillation
by: Zisman, Ilya, et al.
Published: (2023)
by: Zisman, Ilya, et al.
Published: (2023)
MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics
by: Liu, Xinyu, et al.
Published: (2026)
by: Liu, Xinyu, et al.
Published: (2026)
Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought
by: Ildiz, Muhammed Emrullah, et al.
Published: (2026)
by: Ildiz, Muhammed Emrullah, et al.
Published: (2026)
Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning
by: Zhang, Kehao, et al.
Published: (2026)
by: Zhang, Kehao, et al.
Published: (2026)
Reinforcing Chain-of-Thought Reasoning with Self-Evolving Rubrics
by: Sheng, Leheng, et al.
Published: (2026)
by: Sheng, Leheng, et al.
Published: (2026)
ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression
by: Bian, Tingcheng, et al.
Published: (2026)
by: Bian, Tingcheng, et al.
Published: (2026)
Similar Items
-
Towards Provable Emergence of In-Context Reinforcement Learning
by: Wang, Jiuqi, et al.
Published: (2025) -
Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning
by: Xie, Zixuan, et al.
Published: (2026) -
Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift
by: Liu, Xinyu, et al.
Published: (2026) -
Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
by: Xie, Zixuan, et al.
Published: (2025) -
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)