Qwen3.5
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Headlines · 16
- Developer releases sdft_zero3 for self-distillation under ZeRO-3
Developer ifiaposto released sdft_zero3, a plug-and-play GitHub repository combining self-distillation with an EMA teacher, ZeRO-3, and PEFT. The author says it includes LoRA adapter switching for teacher and student passes, resumable EMA teacher checkpoints, and DeepSpeed configurations intended to address memory spikes. The author reports testing the method with Qwen3.5 and ToolAlpaca.
r/MachineLearning · 🔥 0 - Nvidia open-sources Flora for robot video annotation
Nvidia released Flora on Hugging Face as an open data engine for labeling robot videos, combining action segmentation, captioning, progress ratings and task-outcome evaluation. Built from three fine-tuned Qwen3.5 models, Nvidia says Flora is already used to annotate up to 200,000 hours of video per day during GR00T model training. Nvidia estimates labeling costs of $0.72 per video hour versus about $88.50 for GPT-6 Astra, although these are projections rather than production measurements. Its benchmarks show strengths in action boundaries and mid-episode error detection, but GPT-6 Astra remains stronger in description accuracy and some robot-video results.
The Decoder · 🔥 0 - OpenMobile-2 releases open smartphone-agent models and training environment for more than 110 apps
Researchers from Nanjing University, SenseTime, and other universities released OpenMobile-2, including 9B and 27B models built on Qwen3.5 and Qwen3.6, together with the training environment, data, and reproduction instructions. The environment covers more than 110 simulated applications. The researchers reported that, with the same amount of training data, increasing app diversity raised success on unseen applications from 60.7% to 67.5%. They also reported that the 27B model scored 59.6 on SPA-Bench, the highest result among open models. These findings come from one secondary report and remain researcher-reported results.
The Decoder · 🔥 0 - miHoYo and universities open-source GameScaling game-agent models from 0.8B to 27B
IT Home reports that miHoYo's Honkai AI team, together with Tsinghua University, Peking University, the University of Chinese Academy of Sciences, the University of Hong Kong and the National University of Singapore, has released the open-source GameScaling series of game-playing models. The team published eight checkpoints at 0.8B, 2B, 9B and 27B parameters, each in an S1 version trained only on Minecraft data and an S3 version trained on mixed-game data with Minecraft at 27%, all using 1,600 hours of gameplay. According to the project's GitHub description, the models are continually pre-trained from the Qwen3.5 vision-language model and take current and earlier frames plus input history to emit keyboard and mouse actions, with each call covering about 200 ms including six 33 ms steps; tasks span mining, crafting and combat in Minecraft, fighting and pathfinding in Doom, and escape-room puzzles. Code and weights are under Apache 2.0, vLLM serving is supported, and the 0.8B, 2B and 9B weights fit on one GPU while 27B needs two GPUs with tensor parallelism, the report says.
IT之家 AI · 🔥 0 - TorchTitan Adds New Model Support, TitanRL and Scaling Improvements
PyTorch reports that TorchTitan, a PyTorch-native platform for generative AI pretraining and post-training, now supports Muse Spark Open, Muse Glimmer, Qwen3-VL, Qwen3.5, Kimi K2.7, Kimi K3 and DeepSeek-V4 through full training-stack integrations. The update adds the TitanRL reinforcement-learning stack, asynchronous rollouts, FlexShard and Dist-MoE integrations, and distributed checkpointing improvements. PyTorch also reports internal benchmarks for these features, but the announcement is a single vendor source and does not provide an independent comparison with other training frameworks.
PyTorch Blog · 🔥 0 - TokenGate adds learnable token pruning to speed long-context prefill
Researchers posted a paper on r/LocalLLaMA describing TokenGate, a small drop-in component that extends already-trained models to enable end-to-end learnable token pruning over long-context inputs for faster and more memory-efficient prefill. To learn despite the non-differentiable top-K operator, the authors introduce token-gated attention, a Mixture-of-Experts-inspired mechanism that re-weights attention logits using token importance scores. They report 1.4–11.3× faster time-to-first-token and 1.5–3.4× lower peak memory with no accuracy degradation for prefill, and up to 6.4× faster prefill and 2× lower peak memory than the best prior token-pruning methods, all figures self-reported. The authors say TokenGate natively extends standard transformers such as Qwen3 and hybrid-attention models such as Qwen3.5 with lightweight adaptation, and they released code on GitHub.
r/LocalLLaMA · 🔥 0 - Ornith releases 1.5-122B with an expanded self-improvement loop
Ornith introduced Ornith-1.5-122B, extending its earlier model by jointly optimizing task generation, scaffold construction, and solution rollouts. The developer says the model continuously creates training tasks, discovers solution strategies, and improves its policy through reinforcement learning, with official and Unsloth GGUF files linked in the announcement.
r/LocalLLaMA · 🔥 0 - Developer launches Tinkerbox to run decision models on Apple's Neural Engine via Shortcuts
A developer posting on r/LocalLLaMA as /u/WeGoToMars7 released Tinkerbox, an app that runs decision models on Apple's Neural Engine (ANE) and exposes Shortcuts actions for automating system events such as incoming email and iMessages, folder and file changes, time of day and alarms. The author says LiquidAI d1-3B runs on the ANE with roughly 40 ms latency for warm short-context decisions under 256 tokens, measured on an M5 Pro, and says the model sits on the Pareto frontier of Decision Index 0.3.2; both are self-reported or cited by the developer, who notes he has no other Macs to test on. The d1-3B implementation uses a tree structure to evaluate multiple decisions in a single pass, source code and AOT-compiled models are published on GitHub and Hugging Face, and the author says he is working on integrating vLLM d3 models based on Qwen3.5 with 0.8B, 2B and 4B variants planned.
r/LocalLLaMA · 🔥 0 - Ollama v0.40.7-rc0 adds MLX support for PPLX Decider v1.2
Ollama tagged release v0.40.7-rc0 (commit 8be580a, tagged by dhiltgen on 17 Oct) with a title stating that the MLX component adds support for PPLX Decider v1.2 via PR #19072. The release description says the change implements a Qwen3.5-backed decision scorer with noncausal attention, a publisher readout, image handling and System One prompt encoding. Those capability details are stated only in the release text and have no independent verification.
Ollama Releases · 🔥 0 - Surface Laptop Ultra hands-on: RTX Spark N1X, 128GB unified memory and local LLM tests
After Microsoft's Windows and Surface event in San Francisco on October 7, ifanr obtained the Surface Laptop Ultra and tested it. The machine runs Nvidia's RTX Spark N1X platform, which Nvidia says offers up to a 20-core Grace CPU, a Blackwell GPU with 6,144 CUDA cores and up to 128GB of unified memory, with a claimed 1 PFLOPS of sparse FP4 AI compute; the Surface Laptop Ultra and the first wave of RTX Spark OEM machines opened for preorder and go on sale October 16. Running Qwen3.5 35B-A3B at Q4 quantization locally via Ollama and Open WebUI, the reviewer measured about 59.65 tokens per second of sustained generation, and about 20.98 tokens per second for the denser Qwen3.8 27B after warm-up; a local version of WorkBuddy can use on-device compute for agent tasks, some of them offline, but it does not yet switch automatically between local and cloud models. The laptop has a 15-inch 3:2 Mini LED touchscreen with 2000-nit peak HDR brightness, is about 18mm thick and 2.0kg, and starts at 21,988 yuan for 24GB of memory and 512GB of storage, rising to 48,388 yuan for the 128GB top configuration; Asus, Lenovo, MSI, Dell and HP also announced RTX Spark models.
爱范儿 · 🔥 0 - APUS open-sources Reflex Agent, a decision-model-driven personal agent
On October 13, APUS AI (Qilin Hesheng) announced that it had open-sourced its personal agent Reflex Agent under the MIT license, with desktop, Web and SDK access. The system uses APUS's own Reflex decision model—reported to be a 4.5B-parameter model fine-tuned from Qwen3.5—for model routing, skill selection, result review and execution supervision, and combines it with the Continuum memory engine's unlimited memory, lossless memory and lossless-input layers for long-horizon tasks. APUS AI Lab says response speed can reach up to 3x that of mainstream agents such as Claude Code and Codex in its test scenarios; APUS says "infinite context" means agent memory is no longer fully limited to a single model window, not that a single call's input limit has been exceeded.
量子位（原生 RSS） · 🔥 0 - Microsoft Research: clearing chat history helps AI agents escape 'idea basins'
A Microsoft Research paper, Fork-and-Flush: Escaping Idea Basins in Autoresearch Agents, posted to arXiv on October 5, finds that long-running autoresearch agents can be constrained by accumulated conversation context, keeping them refining similar solutions inside what the authors call "idea basins." Eight independent runs of the same agent on the same task plateaued at final scores ranging from 68.3 to 100.0, and the gaps persisted even with substantially more compute, according to the reports. The proposed Fork-and-Flush method splits the agent into parallel branches that inherit the workspace and generated files but lose all chat context, then keeps the best-scoring branch. The paper reports a normalized average score of 0.78 on 13 long-running tasks at the same compute budget, versus 0.47 for single runs and 0.54 for best-of-4, while flushing context without branching improved results by about 22 percent.
The Decoder · 🔥 0 - Tencent open-sources EVIE-8B and EVIE-4.5B visual document retrieval models
Tencent published EVIE-8B and EVIE-4.5B on Hugging Face, open-sourcing weights, training and distillation pipelines, the HAC token-compression algorithm and evaluation suites under Apache-2.0, with a technical report at arXiv:2610.11553. Both build on a ColQwen3.5 late-interaction multi-vector architecture: EVIE-8B produces 4096-dimensional per-token embeddings, while EVIE-4.5B uses a single 2048D projection whose Prefix-MRL width can be truncated at runtime to 64–2048 dimensions and is distilled from the 8B teacher. The model cards report 66.75 nDCG@10 on ViDoRe V3 for EVIE-8B and 66.02 for EVIE-4.5B, and say HAC compression cuts page representations from roughly 750 vectors to 32, giving about 3.81 GiB of index per million pages; however, a leaderboard table in the same cards lists the two models at 66.24 and 65.70 Mean Task, which does not match those figures.
腾讯混元 · Hugging Face · 🔥 0 - Hugging Face author builds six models with ML Intern for about $103
In a Hugging Face blog post, author yuvraj sharma says he switched on ML-intern mode in HuggingChat and used natural-language prompts to have the agent plan the work, request a budget, run a smoke test, then train, evaluate and publish models on the Hub. The first project distilled the 9B prompt rewriter bundled with Qwen-Image 2.1 into a 0.8B version that runs on a CPU; the author reports valid output 99.7% of the time, about a quarter of the teacher's token use, and $16 in compute including labeling 8,797 example requests. He then produced five more projects the same way, including a FLUX.2 klein base 4B LoRA, a camera-angle LoRA for Qwen-Image 2.1, a doodle-in LoRA and an Agate 4-step model, with a listed total compute cost of about $103.
Hugging Face · 🔥 0 - V-CoLA: training-free vision token compression for linear-attention VLMs
V-CoLA is a training-free vision token compression framework designed for hybrid architectures that use linear attention (the paper cites Qwen3.5 as an example). It introduces a uniqueness-aware importance criterion to pick critical vision tokens and an adaptive token merging strategy to compress them, with all components tuned to stay compatible with the chunk-wise parallelism of linear attention. The authors report reaching 99.5% of original performance with only 50% of vision tokens, over 88% with as few as 12.5%, and 1.86x to 6.15x prefill speedups across multiple benchmarks. They also note that prior softmax-attention-based attention or similarity compression methods degrade noticeably in this regime.
Hugging Face · Papers · 🔥 0 - OpenTPU open-sources an AI accelerator built by AI
FeSens has published OpenTPU, an open-source AI accelerator whose hardware, instruction set, simulator, compiler, and host software are contained in one monorepo. The project reports running models including LFM2.5-230M, Qwen3, Qwen3.5, Gemma 4, SmolLM3, and Phi-4-mini on an FPGA card with bit-for-bit agreement between the simulator and hardware.
Hacker News · AI（100+ 分） · 🔥 0
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