Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
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Ring-Zero studies reinforcement learning with verifiable rewards (zero RL, i.e. no SFT warm-start) scaled up to a trillion-parameter model, presenting a stable training pipeline that fixes issues like poor readability and token redundancy in reasoning traces. Scaling is shown to improve sample efficiency and produce emergent behaviors such as self-verification and parallel reasoning on math benchmarks.
Why it matters
One of the largest published zero-RL scaling studies to date, offering evidence for how RL-only training regimes behave as parameter count grows into the trillion range.
Importance: 2/5
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