Data & training

The training side: datasets, synthetic data, pre- and post-training methods, compute and training cost.

Latest picks

61–80 of 124

May 19Tuesday

AI HOT (Curated Pool)May 19, 00:00

NVIDIA fine-tunes Cosmos Predict 2.5 with LoRA/DoRA for robot video generation

NVIDIA published a Hugging Face post on fine-tuning Cosmos Predict 2.5 with LoRA and DoRA to generate robot first-person videos from text prompts; the post does not disclose dataset size, training cost, or evaluation results.

Why it matters: HKR-H/K/R pass: the robot POV video angle is clickable, and LoRA/DoRA on Cosmos Predict 2.5 is a concrete mechanism. Missing dataset scale and metrics keep it in the low featured band.

May 18Monday

Synced · WeChatMay 18, 12:47

ICML 2026: Huawei GTS proposes EDCO for dynamic curriculum fine-tuning

Huawei GTS proposed EDCO, a dynamic curriculum method that selects fine-tuning samples by inference entropy; prefix entropy estimation cuts per-sample scoring time from 2.24 seconds to 0.37 seconds.

Why it matters: HKR-H/K/R pass: the story has a lab-race hook, a concrete entropy-based mechanism, and a 2.24s→0.37s efficiency claim. It stays below 78 because it is still a training-method paper, not a major model or product release.

Take

EDCO’s useful move is not the “difficulty-adaptive training” label. It plugs sample selection into the fine-tuning loop and makes the scoring cost survivable. The concrete hook is strong: full-sequence entropy costs 2.24 seconds per sample, prefix entropy drops it to 0.37 seconds, and 8-GPU parallel scoring reaches 0.04 seconds. On Datacom RLFT, accuracy moves from 40.43% with random sampling to 46.96%.

I don’t buy the “new paradigm” framing yet. Active learning, curriculum learning, and hard-example mining have been circling this idea for years. The LLM-specific question is whether inference entropy stays a reliable proxy for useful gradient signal. The paper tests Qwen3-4B, Llama3.2-3B, and three vertical domains; it does not settle larger models, longer training, or production distribution drift.

r/LocalLLaMAMay 18, 10:14

I trained TIME: short context-triggered thinking on Qwen instead of overthinking

An independent author trained TIME with QLoRA on Qwen3 4B/8B/14B/32B to trigger short mid-response reasoning when context changes; the post says datasets, notebooks, scripts, curriculum, and TIMEBench are public, with 24GB VRAM enough for training up to 14B.

Why it matters: HKR-H/K/R all pass: the post has a clear tuning hook, concrete reproducible details, and strong local-LLM resonance. Reddit single-post sourcing keeps it in the 72-77 featured band, below lab-level releases.

AI HOT (Curated Pool)May 18, 08:00

Composer 2.5 release and technical analysis

Cursor released Composer 2.5, built on a Moonshot open-source checkpoint, trained with synthetic data from real codebases at 25 times the previous scale, and updated with text-feedback reinforcement learning and a sharded Muon optimizer.

Why it matters: HKR-H/K/R all pass: Cursor is a core coding-agent surface, and the post gives concrete training details around Moonshot, 25x data, RL, and Muon. It lacks benchmarks, pricing, or user-facing capability limits, so it stays in the 78–84 band.

May 17Sunday

Google DeepMindMay 17, 21:50

Google DeepMind launches Gemini for Science toolset

Google DeepMind released Gemini for Science, which includes three experimental tools on Google Labs: Hypothesis Generation, built on Co-Scientist.

Why it matters: Google is packaging research prototypes like Co-Scientist and AlphaEvolve into apply-to-use science tools, showing what agentic research looks like in practice.

QbitAI · WeChatMay 17, 18:22

TGO Aligns Visual Generative Models with Scalar Feedback Without Preference Pairs | ICML 2026

NUS proposed Threshold-Guided Optimization, which converts scalar feedback into positive or negative updates through a score-distribution threshold and was accepted by ICML 2026; experiments cover Stable Diffusion v1.5, FLUX, Wan 1.3B, and Meissonic across image and video generation settings.

Why it matters: HKR-H/K/R pass: the paper has a concrete mechanism and tests across SD v1.5, FLUX, Wan 1.3B, and Meissonic. Impact is research-heavy, so it lands in featured, not must-write.

Take

TGO matters because it treats visual feedback as scores, not forced winner/loser pairs. The mechanism is simple: a score-distribution threshold sets update direction, and distance from that threshold sets weight. The paper tests Stable Diffusion v1.5, FLUX, Wan 1.3B, and Meissonic, so this is broader than a one-backbone diffusion trick.

I don’t buy the “new paradigm” framing. PMPO already loosens unpaired positive/negative feedback, and QRPO handles pointwise absolute rewards through quantiles. TGO is the visual-generation engineering compromise: cheap, readable, and easy to plug into diffusion or masked generators. The weak spot is the global threshold. It compresses prompt difficulty, style taste, and reward-model bias into one cutoff. If the scorer is skewed, pseudo-negatives will suppress minority aesthetics with mathematical confidence.

Dwarkesh Patel podcastMay 17, 03:01

Notes on Pretraining Parallelisms and Failed Training Runs

Dwarkesh documents pretraining failure modes and parallelism tradeoffs: expert choice and token dropping can break causality in MoE routing, FP16 collectives can bias repeated additions after values exceed 1024, pretraining FLOPs are given as 6ND, B300 HBM is listed as 288GB, and FSDP communication can reach params × 3 with reduce-scatter.

Why it matters: HKR-H/K/R all pass: Dwarkesh’s notes expose concrete pretraining failure modes and numbers. The systems-training focus is specialized, so it sits in the high-quality band rather than same-day must-write.

May 15Friday

r/LocalLLaMAMay 15, 07:09

I trained Qwen3.5 to jailbreak itself with RL, then used the failures to improve its defenses

The author built an RL-based automated red-teaming loop for Qwen3.5, raising defense rate from 64% to 92% while benign accuracy fell from 92% to 88%, and the attacker found 7 tactic families.

Why it matters: HKR-H/K/R all pass: a named first-person RL red-team loop with concrete rates and failure modes. Source is a single Reddit post without paper/code validation, so it stays below P1.

Take

This is less “Qwen3.5 learned to defend itself” than a clean reminder that RL red teams optimize the loopholes in your reward. The concrete bit matters: plain GRPO collapsed into the same fiction-writing jailbreak, then tactic clustering plus reward dilution by cluster size produced 7 tactic families. That is the useful mechanism here, not the headline defense gain.

The numbers are still a decent sanity check: defense rate moves from 64% to 92%, while benign accuracy drops from 92% to 88%. A 4-point benign hit is not free; it is the tax you pay for broader refusal behavior. I like that the author reports it. I do not treat this as a benchmark result yet. Test-set size, harm taxonomy, holdout separation, and evaluator setup are not disclosed in the snippet, and those decide whether this survives outside a Reddit demo.

r/LocalLLaMAMay 15, 06:55

I Let a Small Model Train on Its Own Mistakes; It Reached 80% on HumanEval and Beat GPT-3.5 on Math

The author fine-tuned Qwen 2.5 7B base on self-mined mistake-correction pairs, raising HumanEval from 25/164 to 112/164; Qwen 2.5 14B used 100 pairs and a 95-minute H100 run costing $3.50.

Why it matters: HKR-H/K/R pass: the hook is strong and the post gives samples, H100 time, cost, and HumanEval deltas. Kept at 78 because it is a single Reddit post and the 80% claim differs from 112/164.

r/LocalLLaMAMay 15, 01:33

MOOSE-Star (ICML 2026): 7B Model and 108K-Paper Dataset for Scientific Hypothesis Discovery

MiroMind researchers released the MOOSE-Star collection with three 7B models and TOMATO-Star, a dataset of 108,717 NCBI papers. MS-IR-7B reaches 54.37% inspiration-retrieval accuracy, uses DeepSeek-R1-Distill-Qwen-7B as its base, runs at about 14GB fp16, and supports llama.cpp, vLLM, and SGLang.

Why it matters: HKR-H/K/R all pass via the local 7B research-agent hook and concrete dataset metrics. Single Reddit source and limited lab gravity keep it below the must-write band.

Take

MOOSE-Star’s useful claim is not “AI discovers science.” It puts a small, runnable baseline under a noisy category. The concrete hooks are good: three 7B models, TOMATO-Star with 108,717 NCBI papers, MS-IR-7B based on DeepSeek-R1-Distill-Qwen-7B, 54.37% inspiration-retrieval accuracy, about 14GB fp16, with llama.cpp, vLLM, and SGLang support.

I like the 7B choice. Scientific hypothesis agents get muddy fast when the demo rides a closed frontier model; nobody can separate retrieval, reasoning, memorization, and prompt craft. A 14GB fp16 model lets labs run ablations instead of applauding a slide. The Reddit body is blocked by 403, so training details and benchmark construction are not visible here. I would be careful with the 54.37% number until we see negatives, time splits, and leakage controls.

May 14Thursday

r/LocalLLaMAMay 14, 18:32

Automated AI researcher running locally with llama.cpp

Hugging Face’s ml-intern added local-model support through llama.cpp and ollama; the post says Qwen3.6-35B-A3B can orchestrate CPU/GPU sandboxes and Hub jobs to run an end-to-end SFT workflow.

Why it matters: HKR-H/K/R all pass, but this is a Reddit-sourced open-source tool update, not a major model release. Local sandbox and Hub-job orchestration for SFT put it just above the featured threshold.

Take

This reads like a local agent plumbing milestone, not proof that “AI researcher” is solved. The concrete hook is ml-intern adding llama.cpp and ollama support, with Qwen3.6-35B-A3B orchestrating CPU/GPU sandboxes plus Hugging Face Hub jobs to finish an end-to-end SFT run. Reddit’s body is blocked by 403, so run time, failure rate, human intervention points, and VRAM setup are missing. I’d treat this as a reproducible toolchain claim, not a capability claim. Claude Code and OpenAI’s Codex-style agents already made tool use feel normal in cloud setups; the local angle buys privacy, cost control, and sandbox ownership. The weak spot is still long-horizon brittleness: one bad dependency, dataset format issue, or Hub auth error can turn a 35B-A3B “researcher” back into a script runner.

Xinzhiyuan · WeChatMay 14, 13:05

Yuandong Tian and Seven Co-Founders Launch Recursive Superintelligence at $4.65B Valuation

Recursive Superintelligence, founded by Yuandong Tian and seven other AI researchers, has a 25-person team, $650 million in funding, and a $4.65 billion valuation, with a stated goal to automate evaluation, data filtering, training, post-training, and research-direction selection.

Why it matters: All three HKR axes pass: a $650M raise at a $4.65B valuation for a 25-person recursive-improvement startup is not routine funding. The stated target spans evals, data selection, training, post-training, and research selection.

Take

Recursive Superintelligence’s valuation is loud, but the bet is not silly: automate evaluation, data selection, training, post-training, and research-direction choice as one loop. A 25-person team raising $650M at a $4.65B valuation with no product looks absurd. With Yuandong Tian, Richard Socher, Jeff Clune, and ViT first author Alexey Dosovitskiy, investors are paying for a shot at replacing parts of the frontier-lab workflow.

I don’t buy the “AI researchers lose their jobs” framing. The sharper threat is that expensive human judgment inside model iteration gets eaten by tooling. DeepMind’s AlphaEvolve and Darwin Gödel Machine already showed algorithm search and self-editing code can move benchmarks. Nathan Lambert’s lossy self-improvement critique is also fair: nobody sane lets agents burn multi-billion-dollar training budgets unsupervised. Recursive has to prove stable savings in elite researcher time, not science fiction.

Synced · WeChatMay 14, 12:10

China in Focus: PsiBot Uses 100,000 Hours of Human Data for Embodied AI

PsiBot says it uses 100,000 hours of human operation data to train robot policies, with the W0 world model acting only as a training-time transfer module while deployment runs R2 alone.

Why it matters: HKR-H/K/R all pass, but the facts come mainly from company framing and lack an artifact link, benchmark, or third-party replication. This fits a solid robotics research/product story, not the 78+ band.

Take

PsiBot’s bet is embodied AI’s version of buying data scale first, then paying the transfer tax. The concrete setup is clean: 100,000 hours of human operation data, W0 as an action-conditioned world model during training, and R2 alone at deployment. That avoids the latency and reliability mess of running a world model on the robot. Compared with tens of thousands of hours of teleoperation, human-centric capture can enter cashier, warehouse, and factory workflows.

I don’t buy the “similar effect” claim yet. The article gives about 14,600 Hugging Face downloads for SynData, but no task suite, success rate, repeated real-robot trials, or hardware-controlled comparison. Robotics companies have spent two years using demos to dodge metric debt; Figure, 1X, and Tesla all did versions of it. PsiBot’s architecture is smart. The evidence is still mostly company narration.

Synced · WeChatMay 14, 12:10

ACL 2026: Alibaba DAMO I²B-LPO Improves RLVR Exploration

Alibaba DAMO Academy introduced I²B-LPO, an RLVR post-training framework that branches rollouts at high-entropy nodes and filters them with an information-bottleneck self-reward, reporting up to 5.3% accuracy gains and 7.4% semantic-diversity gains on math benchmarks using Qwen2.5-7B and Qwen3-14B.

Why it matters: HKR-H/K/R all pass: the ACL 2026 DAMO paper has a clear RLVR exploration hook, concrete I²B-LPO mechanics, and benchmark gains. It is still a training-method paper, not a major model or product release, so 78 fits the lower good-quality band.

May 13Wednesday

AI HOT (Curated Pool)May 13, 14:19

SenseNova-U1 Technical Report Released: Guide to Native Multimodal Model Building

SenseTime released the SenseNova-U1 technical report, covering six-stage training, RL post-training, and distillation; the open-source SenseNova-U1-A3B-MoT uses an MoE architecture and activates only 3 billion parameters.

Why it matters: HKR-H/K/R all pass: A3B-MoT’s 3B active parameters and six-stage training recipe give concrete signal. The score stays near the featured floor because this is a vendor post with no benchmarks, license terms, or reproduction details disclosed.

Take

SenseTime’s useful move here is not the “native multimodal” branding. It is exposing the pipeline: six-stage training, RL post-training, distillation, and pixel-space flow matching. SenseNova-U1-A3B-MoT activates only 3B parameters, which gives practitioners a concrete cost claim to test rather than another giant-model slogan.

I don’t buy the “full guide” framing yet. The snippet says weights, code, demo, and the technical report are open, but gives no benchmark table, training-token count, vision resolution, total expert count, or routing details. Qwen-VL, InternVL, and LLaVA-style releases already trained the field to demand reproducible evals, not architecture poetry. SenseTime’s bar is simple: show that 3B active parameters survive real multimodal tasks without hiding behind selective demos.

Xinzhiyuan · WeChatMay 13, 12:06

Tsinghua-affiliated team open-sources MiniCPM-V 4.6, a 1.3B model tunable on one RTX 4090

ModelBest, Tsinghua University, and OpenBMB open-sourced MiniCPM-V 4.6, a 1.3B multimodal model that supports full fine-tuning on one RTX 4090 and offers 4x/16x visual token compression for accuracy or speed trade-offs.

Why it matters: HKR-H/K/R all pass: the story gives a concrete open-source multimodal release with size, hardware condition, and token-compression details. It lowers local fine-tuning cost, but it is not a frontier-lab flagship release, so 78–84 fits.

May 12Tuesday

AI HOT (Curated Pool)May 12, 23:54

How Open Model Ecosystems Compound

China’s open AI model community forms a self-reinforcing loop, with domestic open model downloads rising by more than 200% quarter over quarter.

Why it matters: HKR-H/K/R all pass: the flywheel framing is clickable, the article gives a >200% QoQ download claim, and the topic hits China open-model competition. It is strong commentary, not a model launch, so 78 featured.

Take

China’s open-model advantage is not free weights; it is cheaper failure. Lambert cites Ai2 and Epoch AI estimates that roughly 80% of frontier compute goes into R&D, not the final training run. If leading Chinese labs keep publishing technical reports, weights, and infra details, peers avoid rerunning the same MoE, RL, and data experiments.

I don’t buy the lazy “open is cheaper” framing. The piece says off-the-shelf open models are usually more expensive for product teams than closed hosted APIs, because inference scale still favors integrated providers. The weak link is the stack: at-scale RL for MoE models still lacks a truly open recipe, and semi-open tools like Thinking Machines’ Tinker or Prime Intellect Lab may not release enough surface area. The compounding only works if labs stop forking the valuable training stack back behind company walls.

Google DeepMindMay 12, 22:40

Google DeepMind publishes Co-Scientist multi-agent research system

Google DeepMind published Co-Scientist research in Nature, introducing a Gemini-based multi-agent AI system that iteratively generates, debates and evolves new hypotheses for complex scientific problems.

Why it matters: The post discloses the system's three-stage collaboration mechanism and deployment cases at several labs, showing how AI takes part in scientific hypothesis generation.

QbitAI · WeChatMay 12, 11:55

Shanghai AI Lab Study: SFT Generalizes Under Three Conditions

Shanghai AI Lab, Shanghai Jiao Tong University, and USTC tested Long-CoT SFT on Qwen3-14B-Base and found that cross-domain performance recovered and improved after 8 epochs, with generalization conditioned on optimization depth, data quality and structure, and base-model capability.

Why it matters: HKR-H/K/R all pass: the SFT-generalization claim has a clear hook, Qwen3-14B-Base plus an 8-epoch finding, and direct relevance to fine-tuning teams. It lacks deployment impact or full benchmark detail, so it stays in the mid-featured band.

r/LocalLLaMAMay 12, 05:01

Prompt caching for RL training: 7.5x speedup on long-prompt, short-response workloads

The author proposes prompt caching for RL training. On Qwen3.5-4B, it reports a 7.5x speedup with 16k-token prompts and 64-token outputs, and the G=8 example with 1000-token prompts and 100-token responses reduces 8800 processed tokens to 1800 unique tokens.

Why it matters: HKR-H/K/R all pass: the angle is novel, and the post gives 16k/64 plus G=8 token-dedup numbers. Kept at 78 because this is a single Reddit post without independent replication or a paper/code artifact disclosed.