RLTL;DR: Self-improvement by Internalizing Self-generated Feedback
cs.LG, cs.AI, cs.CL, stat.ML
Submitted: 2026-09-29
Updated: 2026-09-29
Terminology
Sources
- Context Bootstrapped Reinforcement Learning
- Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information
- A General Language Assistant as a Laboratory for Alignment
- Rethinking Continual Experience Internalization for Self-Evolving LLM Agents
- Nudging the Boundaries of LLM Reasoning
- Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
- Contextual Drag: How Errors in the Context Affect LLM Reasoning
- STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving
- GLM-5: from Vibe Coding to Agentic Engineering
- iGRPO: Self-Feedback-Driven LLM Reasoning
- Reinforcement Learning via Self-Distillation
- Tulu 3: Pushing Frontiers in Open Language Model Post-Training
- SPICE: Self-Play In Corpus Environments Improves Reasoning
- Understanding R1-Zero-Like Training: A Critical Perspective
- Policy and World Modeling Co-Training for Language Agents
- SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
- Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning
- CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- ECHO: Terminal Agents Learn World Models for Free
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