arXiv Machine Learning
Jul 13
Mach-Mind-4-Flash Technical Report
arXiv:2607. 09375v1 Announce Type: new Abstract: We present Mach-Mind-4-Flash, a 35B-parameter Mixture-of-Experts (MoE) agentic model with 3B activated parameters.
Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution
The paper presents a post‑training method for dynamically routed mixture‑of‑experts (MoE) models that integrates independently optimized specialists efficiently. It introduces calibrated multi‑teacher on‑policy distillation (C‑MOPD) with teacher‑wise loss normalization and clipping, combined with a post‑capacity bounded‑width dispatch, final‑route replay, and asynchronous rollout–training overlap. On the Turing‑20B‑A2B model, the approach achieves a high integration score (0.9988), retains most specialist gains, outperforms shared SFT and Qwen3‑8B on reasoning‑enabled benchmarks, and reduces consolidation time by 2.86×.
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arXiv AI
Jun 26
NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research
arXiv:2606. 26671v1 Announce Type: new Abstract: Post-training alignment determines the reasoning and human preference following capabilities of large language models, yet most existing works withhold detailed data construction, filtering rules and training recipes, which hinders community reproducibility and lightweight model optimization.
arXiv Machine Learning
Sep 30
BASE: Batch-Aware Selection of Experts Using Predicted Removal Error for Efficient MoE Decoding
arXiv:2609.36222v1 Announce Type: new Abstract: Large language models are increasingly expensive to serve. In large-scale serving systems, autoregressive decoding is often bottlenecked by transferrin...
arXiv Machine Learning
Aug 4
Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget
arXiv:2608. 00916v1 Announce Type: cross Abstract: Modern reranking recipes---billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers---have outpaced the training infrastructure available to most academic groups.
arXiv Machine Learning
Jul 22
One Student, Many Teachers: Multi-Task On-Policy Distillation via Soft-Prompt Privileged Context
arXiv:2607. 18293v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) teaches large language models new skills through a teacher that shares the student's backbone and supervises its own rollouts.
arXiv Machine Learning
Sep 23
You Only Need 2/3 of the Chosen Experts: An Empirical Study of Dynamic Expert Pruning in Fine-Grained MoE LLMs
arXiv:2609.25809v1 Announce Type: new Abstract: Fine-grained mixture-of-experts (MoE) architectures have become a mainstream design for open-weight LLMs, with hundreds of experts and increasingly man...
arXiv AI
Oct 5
Lexicographic Multi-Objective On-Policy Distillation
arXiv:2610.02359v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) usually optimizes answer correctness, yet useful language-model behavior also requires high-qua...
arXiv AI
Sep 2
Instella-MoE Technical Report
Instella‑MoE is a fully open Mixture‑of‑Experts language model with 16 billion total parameters and 2.8 billion active parameters per token, trained from scratch on AMD Instinct GPUs. It incorporates a sparsely activated MoE design with Gated Multi‑head Latent Attention and FarSkip‑Collective connectivity, and follows a multi‑stage pipeline that includes pre‑training, long‑context extension, supervised fine‑tuning, direct preference optimization, and reinforcement learning with Multi‑Teacher On‑Policy Distillation. The model achieves an average score of 76.7 on pre‑training benchmarks and 73.2 on instruction‑following, reasoning, math, coding, and chat benchmarks, outperforming comparable fully open and open‑weight models, and its full training pipeline, weights, and code are released for reproducibility.
arXiv AI
Jun 9
Post-Trained MoE Can Skip Half Experts via Self-Distillation
arXiv:2605. 18643v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its dynamic variant further reduces computation by adjusting the activated experts in an input-dependent manner.
Hugging Face Trending Papers
Oct 27
Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data
The paper demonstrates that Mixture-of-Experts (MoE) decoding can avoid the need for calibration data by using a top‑k channel selector instead of a calibrated threshold. Top‑k selection consistently matches or outperforms threshold‑based methods across various read budgets and tasks, eliminating issues like repetition‑loop collapse. Additionally, the authors propose a reader that uses a ternary sketch and low‑rank mapping to recover skipped channels, achieving 98.1% of full‑model accuracy while reading only 17.8% of routed‑expert weights and speeding up MoE blocks by up to 2.69× compared to optimized vLLM.
arXiv AI
Oct 19
A Stronger Teacher Within: Strengthening MoE Models via On-Policy Bidirectional Distillation
The paper introduces MoEvolve, a bidirectional distillation framework that leverages increased activation budgets in Mixture-of-Experts (MoE) models to create a stronger internal teacher. By first restoring a high‑k model and then transferring its enhanced capabilities back to the original low‑k configuration, MoEvolve improves performance across various MoE models and benchmarks, achieving up to 22.2 domain‑average points and outperforming same‑budget RLVR baselines by up to 6.5 points. The resulting low‑k models often surpass their activation‑expanded teachers.
arXiv AI
Sep 30
Interactive-Policy Distillation with Bidirectional Propose-and-Verify
arXiv:2609.36546v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student model on its self-generated trajectories with dense token-level teacher feedback. However, naive OPD ma...