Mach-Mind-4-Flash Technical Report

• URL: http://arxiv.org/abs/2607.09375v1
• Date: Fri, 10 Jul 2026 12:57:08 GMT
• Title: Mach-Mind-4-Flash Technical Report
• Abstract summary: Mach-Mind-4-Flash is a Mixture-of-Experts (MoE) agentic model with 3B activated parameters.<n>Post-training optimization alone without scaling pre-training compute, the model achieves performance on par with or surpassing that of 100B- parameter-class models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present Mach-Mind-4-Flash, a 35B-parameter Mixture-of-Experts (MoE) agentic model with 3B activated parameters. Through post-training optimization alone without scaling pre-training compute, the model achieves performance on par with or surpassing that of 100B-parameter-class models. By introducing scalable agentic interaction environments for large-scale reinforcement learning, the model attains significant performance gains on real-world application tasks. Our pipeline comprises three stages: (1) a unified RL/OPD training infrastructure with dynamic multi-teacher scheduling and operator-level acceleration, delivering 17\% end-to-end training speedup; (2) multiple domain-specific RL experts trained in parallel across Reasoning, General, and Agent tracks, then fused into a single generalist via Multi-Teacher On-Policy Distillation (MOPD) -- a routed reverse-KL objective that eliminates the see-saw degradation of mixed-reward RL; (3) Hybrid Median-length Policy Optimization (HMPO), a single-stage token-efficiency method that compresses reasoning chains by 19--46\% with $\le$0.7 percentage-point accuracy loss. Mach-Mind-4-Flash scores 92.70 on AIME'26, 82.82 on IFBench, 80.74 on Behavioral-SafetyBench, 75.80 on BFCL-v4, 72.31 on BrowseComp-zh, and 84.20 on ClawBench -- leading or matching models with 10--30$\times$ its activated size at a fraction of the inference cost.

Related papers

• SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD [91.71050339479261]
  This report presents an end-to-end optimization practice on the Ascend NPU SuperPOD.<n>We develop a hierarchical optimization framework spanning model-level parallelism, computation-communication orchestration, and low-level kernel execution.<n>The resulting system achieves 34.22% Model FLOPs Utilization (MFU) with a 2.93x improvement over the open-source baseline recipe.
  arXiv  Detail & Related papers  (2026-07-22T13:49:17Z)
• NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research [5.287668610154937]
  Post-training alignment determines the reasoning and human preference following capabilities of large language models.<n>This work presents NebulaExp, a fully transparent, ablation-driven post-training pipeline built on Qwen3-8B-base.
  arXiv  Detail & Related papers  (2026-06-25T07:03:25Z)
• JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency [56.46698214215968]
  JoyAI-LLM Flash is an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performance and token efficiency.<n>JoyAI-LLM Flash is pretrained on a massive corpus of 20 trillion tokens and further optimized through a rigorous post-training pipeline.
  arXiv  Detail & Related papers  (2026-04-03T13:52:38Z)
• PRISM: Demystifying Retention and Interaction in Mid-Training [20.198164159173647]
  PRISM is a comprehensive empirical study of mid-training design choices for large language models.<n>We show that mid-training on approximately 27B high-quality tokens yields consistent gains of +15 to +40 points on math, +5 to +12 points on code, and +6 to +13 points on science benchmarks while preserving general performance.
  arXiv  Detail & Related papers  (2026-03-17T19:04:33Z)
• Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters [169.7981969517903]
  Step 3.5 Flash bridges frontier-level agentic intelligence and computational efficiency.<n>We focus on what matters most when building agents: sharp reasoning and fast, reliable execution.
  arXiv  Detail & Related papers  (2026-02-11T07:53:51Z)
• Scaling Trends for Multi-Hop Contextual Reasoning in Mid-Scale Language Models [0.0]
  We present a controlled study of multi-hop contextual reasoning in large language models.<n>We show that multi-agent systems show the inverse pattern, achieving up to 80% on reasoning tasks where rule-based methods fail.
  arXiv  Detail & Related papers  (2026-01-06T20:18:55Z)
• Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model [100.86587937568832]
  Ring-1T is the first open-source, state-of-the-art thinking model with a trillion-scale parameter.<n>It features 1 trillion total parameters and activates approximately 50 billion per token.
  arXiv  Detail & Related papers  (2025-10-21T17:46:14Z)
• Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning [29.605396813225386]
  We show how reinforcement learning can be used to train agents for multi-turn interactive tasks.<n>Our methodology offers a practical approach for training capable agents for multi-turn interactive tasks using open-weight models.
  arXiv  Detail & Related papers  (2025-08-05T14:30:47Z)
• Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model [56.92219181993453]
  We propose Reincarnating Mix-policy Proximal Policy Gradient (ReMix) to enable on-policyRFT methods like PPO and GRPO to leverage off-policy data.<n>ReMix consists of three major components: (1) Mix-policy proximal policy gradient with an increased Update-To-Data (UTD) ratio for efficient training; (2) KL-Convex policy constraint to balance the trade-off between stability and flexibility; (3) Policy reincarnation to achieve a seamless transition from efficient early-stage learning to steady improvement.
  arXiv  Detail & Related papers  (2025-07-09T14:29:45Z)
• Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs [51.21041884010009]
  Ring-lite is a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL)<n>Our approach matches the performance of state-of-the-art (SOTA) small-scale reasoning models on challenging benchmarks.
  arXiv  Detail & Related papers  (2025-06-17T17:12:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.