Self-Improving Large Language Models via Progressive Experience Evolution

arXiv:2608.02139 · cs.CL, cs.AI, cs.LG · Submitted 2026-08-03 · Read on arXiv

Shijie Ren, Xiting Wang, Meng Li, Yujie Guo, Yunhang Yao, Ziheng Peng, Xunlong Wang, Yuetan Chen, Haoyang Zhou, Yunlong Liang, Fandong Meng

cs.CL, cs.AI, cs.LG

Submitted: 2026-08-03

Comments: 10 pages, 5 figures

Code: https://github.com/rrrsj/SPEE

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Distillation — 26× in this paper · explained in 2 episodes
• Reinforcement Learning — 21× in this paper · explained in 4 episodes
• GRPO — 18× in this paper · explained in 6 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Self-Distillation — 8× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• On-Policy Distillation — 3× in this paper · explained in 3 episodes
• On-Policy Self-Distillation — 3× in this paper
• Self-consistency — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• GRPO Objective — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• On-policy Self-Distillation (OPSD) — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Self-Progressive Experience Evolution — 1× in this paper
• Transferability — 1× in this paper · explained in 4 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
• Training Verifiers to Solve Math Word Problems
• MiniLLM: On-Policy Distillation of Large Language Models
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Distilling the Knowledge in a Neural Network
• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
• Scaling Laws for Neural Language Models
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
• Let's Verify Step by Step
• ReFT: Reasoning with Reinforced Fine-Tuning
• Privileged Information Distillation for Language Models
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning
• Learning to summarize from human feedback
• Kimi k1.5: Scaling Reinforcement Learning with LLMs
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
• RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
• ReAct: Synergizing Reasoning and Acting in Language Models

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