Concept · 22 episode(s)

Self-Play / Self-Evolution

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Definition

Self-play trains a model by having it play against versions of itself — in games, in dialog, in debate — using the improving opponent as an automatic curriculum. It’s how AlphaZero learned chess and a recurring template wherever reward is hard to specify but win/loss is cheap.

Episodes covering this

  1. 207
    An AI Graded Its Own Math Test 94 Percent — It Actually Scored 20
    More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges
    · ·12 min·Jul 08, 2026
  2. 200
    The One Mechanism That Turns Twenty AI Clones Into an Actual Team
    EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
    Zhang, Xu, Dai et al. · Oregon State University; AG2AI·19 min·Jul 04, 2026
  3. 178
    How an AI Reviewer Learned to Stop Going Easy on AI Writing
    The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators
    Iacob, Jovanović, Shen et al. · University of Cambridge·23 min·Jun 26, 2026
  4. 166
    A Router That Beats the Frontier Models It Calls
    Sakana Fugu Technical Report
    Tang, Cetin, Xu et al. · Sakana AI·26 min·Jun 23, 2026
  5. 161
    A Robot That Plays Before You Give It a Job, And Why That Beats Retrying
    Playful Agentic Robot Learning
    Zhang, Ge, Yoo et al. · University of California·19 min·Jun 19, 2026
  6. 159
    Can a Coding Agent Run Its Own Robot Experiments Overnight, With No Human Resetting the Scene?
    ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
    Xiao, Xie, Zhang et al. · NVIDIA·23 min·Jun 19, 2026
  7. 129
    How a Crowd of Anonymous AI Agents Broke a 40-Year Math Record
    Harnessing the Collective Intelligence of AI Agents in the Wild for New Discoveries
    Bianchi, Kwon, Pappu et al. · Together AI·29 min·Jun 11, 2026
  8. 126
    How Coding Agents Can Mine Their Own Failures Into a Self-Targeting Curriculum
    Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills
    Xiao, Jiao, Wang et al. · Shanghai Jiao Tong University·21 min·Jun 09, 2026
  9. 120
    How an AI Agent Rewrites Its Own Tools, Without an Answer Key
    Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts
    Pan, Liu, Lin et al. · City University of Hong Kong·30 min·Jun 05, 2026
  10. 114
    Agents That Rewrite Their Own Weights Instead of Just Taking Notes
    Scaling Self-Evolving Agents via Parametric Memory
    Ren, Luo, Yang et al. · Peking University / Alibaba Group·26 min·Jun 04, 2026
  11. 112
    When an AI Agent Cheats Without Being Told: Inside the Meta-Agent Challenge
    The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?
    Lu, Wang, Wang et al. · Institute of Software·22 min·Jun 04, 2026
  12. 109
    An AI Got Caught Reading the Answer Key, And Why That Catch Matters
    EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
    Chen, Shi, Li et al. · Shenzhen Institutes of Advanced Technology·28 min·Jun 03, 2026
  13. 107
    How a Market of Crippled AI Agents Outscored One Unrestricted Model
    Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions
    Qi, Su, Qu et al. · Harvard·26 min·Jun 03, 2026
  14. 095
    Seven Wins to Zero: How Organizing AI Agents Like a Lab Changes the Search
    AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation
    Gao, Fang, Zitnik · Harvard University·24 min·May 28, 2026
  15. 090
    How MiniMax-M2 Bets That Sparsity Plus Verifiable Rewards Can Match Frontier Agents
    The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
    MiniMax · MiniMax·28 min·May 27, 2026
  16. 078
    Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
    SkillOpt: Executive Strategy for Self-Evolving Agent Skills
    Yang, Gong, Huang et al. · Microsoft·28 min·May 25, 2026
  17. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
    Qumus: Realization of An Embodied AI Quantum Material Experimentalist
    Shi, Zheng, Juan et al. · Princeton University·29 min·May 23, 2026
  18. 065
    One Loop to Optimize Them All: A Universal API for LLM-Driven Discovery
    optimize_anything: A Universal API for Optimizing any Text Parameter
    Agrawal, Lee, Tan et al. · UC Berkeley·27 min·May 22, 2026
  19. 057
    How Uber Caught 206 Leaked Credentials With an LLM-Powered Security Stack
    ADR: An Agentic Detection System for Enterprise Agentic AI Security
    Li, Hu, Xu et al. · Uber Technologies·28 min·May 19, 2026
  20. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
    Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design
    Pepe, Lin, Magka et al. · FAIR at Meta·32 min·May 18, 2026
  21. 042
    An Agentic Scientific Computing System That Actually Remembers What It Learns
    GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms
    Toscano, Chai, Karniadakis · Division of Applied Mathematics·30 min·May 13, 2026
  22. 019
    When the Best Reward Model Trains the Worst Policy: Inside EvoLM
    EvoLM: Self-Evolving Language Models through Co-Evolved Discriminative Rubrics
    Li, Xin, Xiao et al. · University of Washington·26 min·May 06, 2026

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