Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2

Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2

在 Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2(官方 Qwen3.8-27B 经 SFT + SimPO 后训练得到的模型)基础上继续做多轮 SFT 与 RLOO 后训练,专治原版「已有答案却停不下来、写满 94K 也不给最终答案」的问题。本仓库提供 **BF16**、**静态 FP8 Block128**、**NVFP4 W4A16**、**NVFP4 W4A4**、**NVFP4 W4A4-W8A8**(混合精度)五个 safetensors 档,均为完整多模态(文本 + 图像/视频)、内置官方 BF16 MTP,SGLang 免补丁加载(FP8 包另经 vLLM 冒烟验证);另有 **NInfer W4A4 / W4A4-W8A8**( NVFP4-NInfer/ )、**INT8 W8A8**( INT8/W8A8/ ,文本 + 图像)、**INT4 W4A16**( INT4/W4A16/ ,文本 + 图像,vLLM)以及 **GGUF Q2 / Q3 / Q4 LynnStyle(另有 Q8 MTP 版)、Q6 K、Q8 0**( GGUF/ 、 GGUF-NInfer/ ,内置 MTP)。

• *94K 截断大幅减少**:同口径下 GPQA 从 4 降到 1,LCB 从 13 降到 3。
• *三项全面不低于原版**:GPQA **178/198**、MMLU **450/500**、LCB **90/100**(静态 FP8),原版 FP8 为 177 / 444 / 83。
• *量化无损**:BF16、动态 FP8、静态 FP8 三种精度 GPQA、LCB 一题不差。
• *两种投机解码(互斥)**:DFlash2 单请求约 180 tok/s,MTP 约 91 tok/s,不开投机约 46 tok/s。

相关仓库**

GGUF / GGUF-NInfer（llama.cpp、NInfer，内置 MTP）：Hugging Face · ModelScope

NVFP4-NInfer：Hugging Face · ModelScope

训练方法

谱系**:原版 Qwen3.8 27B(FP8;177 / 444 / 83)→ 我们的 SFT + SimPO110 终版,即 EfficientThink 基座(171 / 442 / 89)→ K3 续训 SFT → 第二周 SFT(week2dose,update-225)→ 第一轮 RLOO(182 组)→ 再一轮 SFT(merge-sft-100),得 sft-base-rloo(177 / 448 / 90)→ 第二轮 RLOO(172 组)→ **Coder390**(178 / 445 / 90,动态 FP8 口径)。括号内依次为 GPQA / MMLU / LCB,均为 100K 同口径全量。

训练基座**:本模型以我们自己的 EfficientThink SFT + SimPO 模型(SimPO110 终版)为起点,继续做多轮 SFT 与 RLOO,该基座模型本身是官方 Qwen3.8-27B 的后训练版本,见 Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2。

名字片段 含义

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Coder 代码强化

390 GPQA、MMLU、LCB 三项都到 90 分

EfficientThink 训练目标:去掉无效的长推理尾巴,保留必要的长推理

Opus5.5、GPT6Astra 撰写出题金标与教师模型轨迹

Grok4.7 本次训练的主持人,全程核对、筛选数据

DSV4Pro、K3 其轨迹作为教师模型用于训练;K3 还做了 RLOO 价值评审和一部分出题工作

SFT-RLOO 训练方法:SFT(含 SimPO)基座上,SFT 与 RLOO 交替进行

MTP / DFlash2 内置 BF16 MTP 头 / 配套 DFlash2 投机解码草稿

要解决的问题**:模型在局部已经有答案之后,仍用 Wait / Actually 把同一套推导反复再推,直到写满上下文,最终答案为空。这类题多数不是不会,而是停不下来。

RLOO 数据**:每题采样 8 条轨迹,只保留组内有对有错的题(8 条全对的不入训);组内 0 或 1 条对的,补 1 条审过的短教师轨迹,替换该组最短的一条错轨迹(共 27 组)。最终 172 组、1,376 条:正确 859 条、错误 517 条,其中空答 68 条。

奖励**:惩罚只打在错的一侧,长而正确的推理仍得正奖励,以免压制必要的长推理。

情形 奖励

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短且对(

Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2

A model post-trained with several rounds of SFT and RLOO on top of Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2, which is itself the official Qwen3.8-27B post-trained with SFT and SimPO. It is built to fix the original model's habit of failing to stop: it already has an answer, yet keeps re-deriving until it hits the 94K cap with no final answer. This repository provides five safetensors tiers, **BF16**, **static FP8 Block128**, **NVFP4 W4A16**, **NVFP4 W4A4**, and **NVFP4 W4A4-W8A8** (mixed precision), all fully multimodal (text + image/video) with the official BF16 MTP bundled; all load in SGLang without patches, and the FP8 package is also smoke-tested on vLLM. It also has **NInfer W4A4 / W4A4-W8A8** ( NVFP4-NInfer/ ), **INT8 W8A8** ( INT8/W8A8/ , text + image), **INT4 W4A16** ( INT4/W4A16/ , text + image, vLLM), and **GGUF Q2 / Q3 / Q4 LynnStyle (plus Q8 MTP versions), Q6 K, and Q8 0** ( GGUF/ , GGUF-NInfer/ , built-in MTP).

• *Far fewer 94K truncations**: under the same protocol, 4 → 1 on GPQA and 13 → 3 on LCB.
• *No regression on any suite**: GPQA **178/198**, MMLU **450/500**, LCB **90/100** (static FP8), vs. 177 / 444 / 83 for the original FP8.
• *Lossless quantization**: GPQA and LCB are identical across BF16, dynamic FP8, and static FP8.
• *Two speculative-decoding options (mutually exclusive)**: about 180 tok/s per request with DFlash2 and 91 tok/s with MTP, vs. about 46 tok/s without speculation.

Related repositories**

GGUF / GGUF-NInfer (llama.cpp, NInfer, built-in MTP): Hugging Face · ModelScope

NVFP4-NInfer: Hugging Face · ModelScope

Training method

Lineage**: original Qwen3.8 27B (FP8; 177 / 444 / 83) → our SFT + SimPO110 final, i.e. the EfficientThink base (171 / 442 / 89) → K3 continuation SFT → week-2 SFT (week2dose, update-225) → first RLOO round (182 groups) → another SFT round (merge-sft-100), giving sft-base-rloo (177 / 448 / 90) → second RLOO round (172 groups) → **Coder390** (178 / 445 / 90, dynamic FP8). Numbers are GPQA / MMLU / LCB, all full suites under the same 100K protocol.

Training base**: this model starts from our own EfficientThink SFT + SimPO model (the SimPO110 final) and goes through several rounds of SFT and RLOO, which is itself a post-trained version of the official Qwen3.8-27B: Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2.

Name part Meaning

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Coder Strengthened coding

390 GPQA, MMLU, and LCB all reach 90

EfficientThink Training goal: remove unproductive reasoning tails while keeping necessary long reasoning

Opus5.5, GPT6Astra Wrote the gold answers and teacher trajectories

Grok4.7 Host of this training run; checked and filtered data throughout

DSV4Pro, K3 Their trajectories served as teacher-model data; K3 also performed the RLOO value review and wrote part of the problems

SFT-RLOO Method: alternating SFT and RLOO rounds on an SFT (incl. SimPO) base

MTP / DFlash2 Bundled BF16 MTP head / companion DFlash2 speculative-decoding draft

The problem**: after the model already has a local answer, it keeps re-running the same derivation with Wait / Actually until the context is full, and the final answer is empty. In most of these cases the model can solve the problem; it just does not stop.

RLOO data**: 8 trajectories sampled per problem; only groups with both correct and wrong trajectories are kept (all-correct groups are dropped). Groups with 0 or 1 correct trajectory receive one reviewed short teacher trajectory, which replaces the shortest wrong trajectory in that group (27 groups). Final set: 172 groups, 1,376 trajectories: 859 correct and 517 wrong, including 68 empty answers.

Reward**: penalties apply only to wrong trajectories; long correct reasoning still earns a positive reward, so necessary long reasoning is not suppressed.

Case Reward

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Short and correct (