post-training

该论文提出了一个统一的六维分析框架，系统综述了2017–2026年大模型训练全流程（数据与分词、预训练、SFT与偏好对齐/强化学习）的395篇核心文献，并揭示了跨阶段效应与多个开放问题。

提出 Conceptizer 训练框架，通过约 9K 个基于上下文同义词构建的概念词表，在 next-token prediction 之外增加意义层面的奖励信号，在 Llama 3、Qwen2.5 和 Falcon3（1B-8B）上提升了困惑度与下游知识/情感任务表现。

该论文提出，在KL正则化控制下，策略的log odds隐含其harness（提示、工具、控制逻辑）的偏好，部署性能等于harness潜力上限减去策略失配；实验在MiniWoB++和Terminal-Bench Lite上证明，仅用固定模型比较harness无法预测训练收益，而事先测得的ceiling与mismatch能准确预测。

Google researchers propose a data-free on-policy self-distillation framework where LLMs synthesize their own curriculum of queries and privileged reasoning traces from coarse topic descriptions, achieving self-improvement across text and multimodal benchmarks without external supervision.

Researchers introduce Solvent, an 8B LLM that extends long-horizon reasoning to multi-step retrosynthesis through a two-round post-training curriculum combining RL and iterative route revision, achieving 61.86% across four benchmarks and rivaling frontier trillion-parameter models.

The paper proposes DIAL-S2ST, a post-training method that learns inference-time latency control in simultaneous speech-to-speech translation models via self-distillation from lookahead predictions and a jointly trained LoRA adapter, demonstrating flexible latency control on French-to-English translation using Hibiki with only 10 hours of source speech.

ParetoRSI-Gym 提出一个多领域递归自我改进（RSI）评估框架，在固定模型与训练管线的前提下让智能体自主搜索与筛选数据，结果显示最强系统（Claude Code + Opus-5）在五个领域上带来平均 9.2% 的相对泛化提升，且各智能体展现出独特的数据选择策略。

Modal 发布 Spindle，一个开源、与 Tinker 兼容的训练服务器，可在 Modal 工作区中运行，让现有 Tinker 脚本无需改动即可迁移，并以 GPU 时间计费取代按 token 计费。

This paper identifies 'on-policy audit collapse,' a failure mode where refreshing a component verifier against full-outcome labels on improved-policy data causes the audit to systematically prefer worse verifiers. The authors derive an exact inversion threshold and propose repairing audits by preserving own-failure support in the refresh distribution.

论文提出 Skill Schema Transport (SST) 框架，通过将新领域知识重铸为模型已有技能空间中的可复用程序化 schema 来缓解持续领域适应中的灾难性遗忘，在两个 3B 模型上将 Qwen MATH 流的最大遗忘从 42.41 降至 7.10 个点。

论文提出 LADDER（Layer-Wise Allocation via Damage-Driven Evaluation and Refinement），一种针对大语言模型的非均匀后训练剪枝方法，通过逐层评估最小化每移除参数的性能损失，生成嵌套掩码轨迹，在 70% 以上高稀疏度下表现显著优于基线。

Baseten Labs release mammoth-1 (27B) and elephant-1 (9B), claiming the best decision models in the world, ranking #1 on the Jev Decision Index. The post-training of Qwen3.5 backbones uses a block-bidirectional attention mask for full attention layers and a highly scalable data recipe.

At PyTorchCon 2026, CrusoeAI's Yiwei Song presented Crusoe's LLM post-training platform, including Crusoe Managed RL — a Tinker API-compatible service that lets customers customize open-weights models while Crusoe manages the GPU infrastructure.

The Athanor paper from ByteDance and other institutions shows 4 Claude Opus 5.5 agents, each occupying an 8-GPU machine, autonomously completing post-training in 104 hours with no human intervention, raising Qwen3-4B-Base to official Instruct level (dev set 15.9→67.3, held-out 9.2→48.2).

The paper introduces LoWAgent, a low-resource multilingual benchmark evaluating web agents across English, Hindi, Bengali, Urdu, and Amharic on six localized websites with 3,000 tasks, finding notable performance drops for low-resource languages—especially open-source models—and showing that post-training on benchmark traces improves agentic performance.

论文提出 projected distillation，通过将学生的失败尝试最小编辑为正确目标，实现近似 on-policy 的学习信号，在数学、代码、科学和工具使用等领域显著优于 GRPO 和 on-policy distillation 等基线。

论文提出 OptEP，一种面向混合专家（MoE）语言模型的后训练专家剪枝方法，通过连续松弛优化专家选择变量并结合重构驱动的权重更新，在 Qwen3、GLM-4.5-Air、Mixtral 等模型上显著优于现有剪枝基线，激进剪枝下精度提升最高达 16.20%。

Navalia is a framework for Cowork Agents combining verifiable task synthesis from corporate financial reports with long-horizon post-training (SFT + RL), achieving consistent gains over its Qwen3.6-35B-A3B base model across five agent benchmarks.

在 PyTorchCon 2026 上，Sara Hooker（Coatue/Adaption AI）发表主旨演讲，探讨扩展更大模型的局限以及向自适应智能的转变，涵盖后训练、测试时扩展、自适应计算和硬件协同设计，并展示 AutoScientist 相关案例。

AgenticRoboRL 是一个跨多种机器人执行框架（harness）对机器人规划器进行监督微调与多轮强化学习的后训练框架，在 LIBERO-PRO 上将 9B 规划器成功率从 18.1% 提升至 71.0%，接近使用外部记忆的 Codex (GPT-5.5) 的 72.1%。