Ring-Zero 7/16 (arXiv 2607.12395): 1T-parameter zero RL reaches 94.1% on AIME 2024, 5 spontaneously emergent cognitive behaviors

arXiv 2607.12395 (indexed as a Hugging Face daily paper on 2026-07-16) introduces Ring-2.5-1T-Zero, a model trained with zero reinforcement learning applied directly to a pretrained base model without human-annotated CoT data, relying on RL with verifiable rewards (RLVR). Architecture: two pretrained base models from the Ling-2.5 family — Ling-2.5-1T-Base (1T MoE, 63B activated) and Ling-2.5-flash-Base (104B MoE, 7.4B activated). Four-stage training pipeline: (1) first-stage RL with clipped importance sampling policy gradient and token-level loss to elicit reasoning; (2) self-distillation compressing verbose CoT traces via shortest-correct-rollouts + self-refinement; (3) second-stage RL with sample-level loss normalization for stable, sustained optimization; (4) third-stage RL with tier-based training at three difficulty tiers (4k / 16k / 64k token budgets). Infrastructure: 320x H200 GPUs with Megatron as the training engine, SGLang for rollout, Areal framework for orchestration. Optimizations include mixed-precision control (BF16 body with FP32 attention softmax and LM head) and context parallelism tailored to the model's hybrid MLA + Lightning Attention architecture. Findings: validates the 'bitter lesson' at 1T-parameter scale; reveals a two-phase training dynamic (initial discovery + later sharpening); Ring-2.5-1T-Zero (Second Stage RL) reaches 94.1% on AIME 2024 with competitive numbers on AIME 2025-2026 / HMMT 2025-2026 / IMOAnswerBench; spontaneously develops five cognitive behaviors without explicit supervision: anthropomorphism, structured formatting, self-verification, parallel reasoning, and context anxiety; CoT traces outperform baselines on comprehensibility, reproducibility (5.8-point gain on Qwen-32B distillation vs DeepSeek-R1), and efficiency (less than half the tokens of competitors).