Absolute Zero Reasoner (AZR)

A Absolute Zero Reasoner (AZR) is a self-play reinforcement learning paradigm that enables language models to simultaneously generate reasoning tasks and learn to solve them without requiring any human-curated data.

• AKA: AZR.
• Context:
  • It can typically implement a unified proposer-solver architecture where a single language model both creates and solves reasoning challenges.
  • It can typically verify solution correctness through deterministic Python executors that provide verifiable rewards.
  • It can typically build an automatic curriculum driven by learnability signals that favor absolute zero reasoning tasks at the edge of the absolute zero reasoner's current capability.
  • It can typically incorporate three absolute zero reasoning modes - absolute zero deduction, absolute zero abduction, and absolute zero induction - that collectively span forward inference, trial-and-error back-search, and program synthesis.
  • It can typically ground abstract absolute zero reasoning in executable code through Python program-input-output triples.
  • It can often optimize absolute zero open-ended learning loops through Task-Relative REINFORCE++, a variance-reduced policy-gradient algorithm with multiple baselines for each absolute zero task-role combination.
  • It can often bootstrap from a single trivial function to mastering thousands of complex problems through pure absolute zero self-play.
  • It can often achieve state-of-the-art performance on out-of-distribution benchmarks despite using zero external data.
  • It can range from being a Simple Absolute Zero Reasoner to being a Complex Absolute Zero Reasoner, depending on its absolute zero reasoning mode integration level.
  • It can range from being a Small-Scale Absolute Zero Reasoner to being a Large-Scale Absolute Zero Reasoner, depending on its absolute zero reasoning model size.
  • ...
• Examples:
  • Absolute Zero Reasoner Implementations, such as:
    • Original Absolute Zero Reasoner System demonstrating unified proposer-solver architecture with three absolute zero reasoning modes and Task-Relative REINFORCE++.
    • Qwen-7B-Coder Absolute Zero Reasoner showing how code training prior amplifies general absolute zero reasoning gains across domains.
    • Llama-8B Absolute Zero Reasoner exhibiting emergent step-by-step absolute zero planning through code comments without explicit instruction.
  • Absolute Zero Reasoner Discoverys, such as:
    • Cross-Domain Transfer Absolute Zero Reasoner Discovery demonstrating how absolute zero reasoning skills acquired in coding tasks transfer to math problems.
    • Emergent Planning Absolute Zero Reasoner Discovery showing spontaneous development of ReAct-style absolute zero reasoning without explicit instruction.
    • Task-Specific Strategy Absolute Zero Reasoner Discovery revealing how different absolute zero reasoning modes develop distinct cognitive absolute zero strategies.
    • Safety Challenge Absolute Zero Reasoner Discovery revealing potential alignment issues in self-improving absolute zero systems.
  • ...
• Counter-Examples:
  • Reinforcement Learning from Human Feedback, which requires human preference data unlike absolute zero reasoning.
  • Supervised Fine-Tuning, which depends on labeled datasets instead of self-generated absolute zero tasks.
  • Traditional Self-Play Frameworks, which typically involve competing agents rather than a single unified proposer-solver agent.
  • Human-Designed Curriculum Learning, which uses predetermined task sequences instead of automatic absolute zero curriculum generation.
  • Static Task Generation Systems, which lack the dynamic difficulty adaptation of absolute zero reasoning.
• See: Self-Supervised Learning, Reinforcement Learning Algorithm, Curriculum Learning, Code as Reasoning Environment, Autonomous AI System.

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