Learning from the Near Future: Temporal Self-Distillation for RLVR

arXiv:2604.20733 · cs.LG · Submitted 2026-04-22 · Read on arXiv

cs.LG

Submitted: 2026-04-22

Updated: 2026-10-01

Terminology

• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes

Sources

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• DAPO: An Open-Source LLM Reinforcement Learning System at Scale
• Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
• RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning
• Learning to Reason under Off-Policy Guidance
• Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
• Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
• SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
• ExGRPO: Learning to Reason from Experience
• Reinforced Self-Training (ReST) for Language Modeling
• Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
• Reinforcement Learning via Self-Distillation
• Self-Distilled RLVR
• MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
• Qwen3-VL Technical Report
• EasyVideoR1: Easier RL for Video Understanding
• Group Sequence Policy Optimization
• Soft Adaptive Policy Optimization
• Learning by Distilling Context

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