[AINews] Death of Params: Z.ai CEO Jie Tang on GLM 5.3 and the new Post-training Scaling Law

We’ve covered GLM 5.2 very excitedly before, and Prof Jie Tang’s belief that there will be an open weights Fable-class model by end of the year (spot check - with 134 days left, there are now two 2-3T models ( Qwen 3.8 Max and Kimi K3) with estimates that Fable is 3-7T, and only 2 points higher on the AA index.)

Prof Jie Tang is back on X to tell us that our shorthand for model sizes is no longer enough: “Parameter count is only meaningful alongside three others — how much data you have, where you intend to spend your compute, and who will run the model, under what conditions.”

We have covered Chinchilla (and post-Chinchilla) scaling laws in past LS years, but, so we will skip the history lesson, but it is good to level-set on why Chinchilla’s assumptions were wrong in the Inference Inflection world (no fixed number, between 200-900 toks/param, citing Roberts et al on task dependence).

In short: Memorization prefers more parameters. Reasoning prefers more post-training data and effective depth. GLM-5.3’s big jumps come solely from RL on long horizon environments:

The environments now cover a much broader range of production workflows, with tasks designed around how engineering and research work is actually carried out in practice.Some represent several days of work for an experienced engineer.In an ML infrastructure task, for example, the model may be given the same working environment as an engineer, withaccess to compute clusters, storage systems, internal documentation, codebases, and experiment results. It must diagnose bottlenecks across the training stack, implement optimizations, run experiments, and deliver a measurable end-to-end speedup while preserving correctness. Training on environments at this level pushes the model toward takingownership of substantial work end to end, rather than relying on users to decompose the problem and supervise each step.

For those following the recursive self improvement story, their entire environment and judging and verifier process is synthetic all the way down: As agent capability improves, much of the difficulty in scaling post-training moves from the model to the environment. A useful task environment has to be executable, verifiable, and close to real professional work — and we need many of them, not a handful of hand-built ones. To scale this process, we builtpipelines that synthesize environments end to end, and for a subset of tasks, the RL reward signal as well.Research agents collect task patterns from real work and turn them into runnable long-horizon environments with multi-step dependencies and hidden state; a judge agent then attempts each task to verify that it is actually solvable. Verifiers are synthesized without access to the reference solution, while solver trajectories are used to discover and close reward shortcuts. A verifier that passes oracle, no-op, and unsolved-state checks produces a binary reward reliable enough to train on directly.

To put an end to parameter count obsesssion, Prof Jie identifies 5 knobs of scaling, including MoE sparsity with the new XA-YB notation. He notes that advanced skills (e.g., finding software vulnerabilities) are not retrieval/memorization problems. They require carrying long causal chains (20+ inference steps) without losing the thread. This ability does not live in total parameter count once a certain knowledge-holding threshold is reached.

And it looks like there is much more to go.

AI News for 8/18/2026-8/19/2026. We checked 12 subreddits,

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AI Twitter Recap

Open-Weight Models, Compression, and Benchmark Movement

Ornith-1.5 lands as a serious new open family:@ornith_released** Ornith-1.5in 9B dense, 35B MoE, and 397B MoEvariants under MIT**, with quantized formats including** FP8, GGUF, MLX, and NVFP4**. The headline claim is end-to-end** self-improvement**: the model proposes tasks, generates scaffolds, and produces RL rollouts to create new training experiences. Reported evals are strong across agentic/coding workloads, includingTerminal-Bench 2.1: 86.1,** SWE-Bench Verified: 86**,** DeepSWE: 56**,** HLE: 44.6**, and** Tool Decathlon: 71.2**. The release was quickly wired into serving stacks byvLLMandOllama.Compression continues to get more aggressive without fully collapsing utility:@UnslothAIand@danielhanchenshipped newQwen3.8-27B GGUFs usingDynamic V3, claiming roughly** 10% higher accuracyat the same size and releasing 1-bit quantsthat still retain about 77% of BF16 accuracywhile running on 8GB RAM**. Their new** Divergence-300metric extends top-1% greedy accuracy across longer generations using unseen examples from Terminal Bench**,** DeepSWE**, and related tasks.** Agent and legal eval boards continue to reshuffle**:@arenapublished a Pareto view of** Agent Arena**, where** Claude Opus 5 (High)leads quality, but lower-cost models like Kimi K3**,** GLM 5.2**,** Grok 4.5**, and** GPT-5.6 Lunadefine much of the value frontier. Separately,@ValsAIreported Grok 4.6at#3/49** onLegal Research Bench with48.1%,** 500k context**, tool/image/file support, and relatively low pricing. For open models,@ValsAIalso highlightedGLM 5.3 as**#2 on Terminal Bench**,#3 on Legal Bench, and**#6 on Skills Bench** among open weights.

Agent Harnesses Become the New Competitive Layer

DeepSeek Harness’s minimalism is deliberate, not incomplete: A detailed writeup amplified by@ZhihuFrontierand summarized by@TheTuringPostframesDeepSeek Harness (DSH) as an intentionally thin shell over a plugin architecture calledCordis. The key design choice is that** everything is a plugin**, including the agent loop itself. Early beta users reportedly shipped** 100+ pluginsand filed 400+ issuesin under a week; examples range from a gomoku model testbedto a database agentthat closes the SQL feedback loop by connecting the model to live query execution. The strongest takeaway is architectural: DSH is less “productized assistant” thanopen agent runtime**, optimized for user-extensible tooling, swappable control loops, and business-rule injection.** TrueFoundry open-sources TrueForge and makes the harness-cost argument explicit**:@truefoundry,@omarsar0, and@kimmonismusall covered the launch ofTrueForge, an** MIT-licensed**, self-hostable, vendor-neutral harness for production agents. The stack includes tool orchestration, context management, subagents, code sandboxes, human approvals, and traces, with bothlocal andhosted deployment modes. The technical claim that resonated: on a14-task enterprise benchmark, TrueForge matched** Claude Managed Agentson Opus 4.8while using about 30% fewer tokens**, and routing to** GLM-5.2cut cost by around 75%while preserving accuracy. The broader industry theme—also echoed by@bradenjhancockand@dbreunig via @rseroter—is that thesession/environment/memory/tools layer** is becoming a major source of both differentiation and savings.Managed harnesses are also getting sharper observability and controls:@ClaudeDevsadded** memory support for self-hosted sandboxes**,** domain allow/block controlsfor web tools, and a redesigned multi-agent session viewerwith minimap**,** grouped transcript**, and** cost-per-thread/session**. OpenAI, meanwhile, continues pushing the opposite angle: give teams the harness primitives to embed into their own products.@OpenAIDevshighlighted theopen-source Codex harness as the runtime beneath internal tools, ops dashboards, and custom apps, while@cursor_aishipped cloud-agent UX improvements around persistent goals and long-lived sessions.

Post-Training, Mid-Training, and RL Systems Work

More evidence that scaling is shifting from parameters toward training recipe quality:@kimmonismussurfaced a notable claim from the** zAI/GLMfounder: progress is still scaling, but too much discourse has fixated on parameter count rather than data quality, inference compute, and post-training**. The cited example is** GLM-5.3**, reportedly based on the same core base model/architecture as** GLM-5.2**, but improved substantially via about** one month of extra RL**.** Microsoft’s Agent Lightning points at RL-through-the-harness as a practical recipe**:@omarsar0highlighted** Agent Lightning v1.0**, which connects arbitrary harnesses to RL through an endpoint proxy, handling issues like** retokenization, sample merging, advantage calculation, normalization, and scheduler/backend coordination**. With**~6K training examples** and modest compute, it reportedly movesQwen3.5-9B onSWE-Bench Verified from41.8% to 56.4%.** Mid-training is being treated more explicitly as an optimization surface**:@cwolferesearchlaid out the current practitioner view of** CPT/midtraining**: optimize** data mixture**,** duration**,** stage ordering**,** sequence length**, and even** post-trainabilityrather than just “continue pretraining on better data.” The thread is useful precisely because it frames these as interacting knobs rather than independent tricks.RL infrastructure keeps improving underneath the research:@SergioPaniegoresurfaced work showing on-policy distillation in TRLbecoming 40x fastervia generation buffers, batched teacher calls, and binary logprob encoding;@mikasenghaasannouncedadaptive concurrency** inprl, dynamically adjusting in-flight rollouts over the course of an RL run.

Benchmarks, Retrieval, and Infra Details That Matter in Production

Qdrant’s filterable HNSW vs ACORN is a substantive retrieval systems update:@qdrant_engineargued that filtered ANN should be addressed in theindex, not only at query time. Their** filterable HNSWadds edges between points sharing indexed payload values, keeping filtered subgraphs connected. In their benchmark on a 1% filter over 1M vectors**, they report** 99.8% recall at 1.0msversus 67.7% at 4.7msfor ACORN**. They also note ACORN still helps for** broad valuesand AND filters**, especially atop a graph already optimized for filters.** Sentence Transformers v6.0 reflects the practical move from single-vector to multi-vector retrieval**:@tomaarsensummarized the distinction clearly: dense retrieval compresses each text into one vector, whilemulti-vector retrieval keeps token-level vectors and scores query tokens against document tokens before aggregating best matches. That matters because late-interaction retrieval is increasingly the default tradeoff for quality-sensitive search systems.Production agent latency often has little to do with the model itself:@dair_aisummarized a paper instrumenting ten agentic apps and finding thatnon-LLM components dominate latency in half of them, with** sandbox memory peaking at 28GB/session**,** up to 32x latency variationacross subsystems, and long idle state retention between steps. The optimizations are unsurprising but important: task-aware servingcuts latency 29–40%, state offreduces memory 4.6x**, and** tool-result cachingremoves 35.2%of redundant search calls. Linear and turbopuffer show vector infra creeping into non-search hot paths**:@turbopuffersaid Linear moved its** delta sync read pathfrom Postgresto turbopuffer**, using attribute indexes for permission filters and reducing the largest syncs by about** 8 seconds**.

Google, OpenAI, Anthropic, and the Productization Race

Gemini 3.7 Flash had a strong day on both evals and product integration:@_philschmidand@NewsFromGooglehighlightedGemini 3.7 Flash taking**#1** on Artificial Analysis’sAA-AnalystAgent, with** 60.0% pass^5**,** 70.5% pass@1**,** 77.5% pass@5**,** 1.32s/task**, and**$0.54 average cost** across80 spreadsheet/document-heavy quantitative tasks. Google also pushed it deeper into product surfaces:Gemini chat and Spark,Search-based interactive simulations built on the fly in AI Mode (example), andAI Studio GitHub syncfor build workflows.OpenAI is leaning into low-cost deployment and privacy positioning:@Replitlaunched** Free Modepowered by GPT-5.6 Luna**, which@kimmonismusframed as a meaningful efficiency win: a model that would recently have been SOTA is now cheap enough to be given away broadly. On the enterprise side,@OpenAIintroducedPrivate Safety Processing, aiming to preserve** Zero Data Retentionfor frontier models while still detecting cross-interaction safety risks without human access to the underlying content. Anthropic continues to tighten the developer ergonomics loop**: beyond the managed-agent updates above,@ClaudeDevsadded a** Concise output style**to Claude Code, another sign that product teams are now tuning not just capability but response-shape as a first-class UX variable.

Top tweets (by engagement) Ornith-1.5 release:@ornith_unveiled an** MIT-licensedopen model family from 9B to 397B**, with strong coding/agentic benchmark claims and broad quantization support.** OpenAI privacy/safety infrastructure**:@OpenAIannounced** Private Safety Processingwhile reaffirming Zero Data Retentionfor frontier models. Gemini student push and product bundling**:@GeminiAppoffered a year of Gemini plans to students globally while rolling out new study-oriented features.Claude Code UX update:@ClaudeDevsshipped** Concise mode**, a small but widely noticed improvement for day-to-day coding-agent interaction.** OpenRouter acquisition**:@patrickcconfirmed** OpenRouter is joining Stripe**, a move many interpreted as validation that** token routing/marketplaces**are becoming core infrastructure rather than edge tooling.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen/DeepSeek Open-Weight Inference Speedups

(Activity: 1428):Introducing Qwen3.8-27B Dynamic v3 Unsloth GGUFsThe image is a technical announcement graphic for “Dynamic v3.0 Qwen3.8”, showing Unsloth’s new Qwen3.8-27B Dynamic v3 GGUF post-training quantizations and claiming>10%

higher top-1% accuracy at the same GGUF size versus other providers. It includes a memory table suggesting the model can run from 1-bit quants on ~8GB

RAM up to BF16, plus a chart comparing accuracy across quant sizes; the post links the GGUF release onHugging Face, theDynamic 3.0 docs/benchmarks, and theimage itself. Unsloth emphasizes these are post-training quantization releases only—“we do NOT use QAT or QAD”****—and says the imatrix calibration file is public for independent evaluation and fine-tuning experiments. Comments were mostly positive, but one technical request asked Unsloth to add the previousUD 2.0 quants to the graph so users can compare against what they already have locally. Another commenter asked for deeper diagnostics, specifically per-category andKV-cache quantization KLD numbers, referencing localbench-style reporting.Several commenters requested more detailed quantization evaluation for the new

Qwen3.8-27B Dynamic v3 Unsloth GGUFs, especially a direct graph line comparing against the prior** Qwen 3.8 27B UD 2.0quants. Suggested metrics included KLDand/or top-1 agreement**, which would help users judge whether the new dynamic quantization is materially better than the versions many already have stored locally.A commenter asked for

per-category KLD andKV-cache quantization KLD reporting, referencing the style of breakdowns fromlocalbench.substack.com. This would make the quant quality discussion more actionable by showing which benchmark/task categories or cache-quant settings degrade most under different GGUF quant formats.There was interest in the practical memory footprint of the quants: one user noted

~15 GB

for Q4_K_M, while another inferred that** IQ4_XS may now fit on**16 GB

VRAM“without mtp.” The technical concern is whether these smaller formats maintain model quality closely enough to justify running a 27B-class model fully on common consumer GPUs.