GLM-5.3 发布：唐杰谈后训练新缩放定律，参数不再是唯一指标

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, with access 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 taking ownership 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 built pipelines 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.

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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-1.5 in 9B dense, 35B MoE, and 397B MoE variants 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, including Terminal-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 by vLLM and Ollama.
• Compression continues to get more aggressive without fully collapsing utility: @UnslothAI and @danielhanchen shipped new Qwen3.8-27B GGUFs using Dynamic V3, claiming roughly 10% higher accuracy at the same size and releasing 1-bit quants that still retain about 77% of BF16 accuracy while running on 8GB RAM. Their new Divergence-300 metric 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: @arena published 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 Luna define much of the value frontier. Separately, @ValsAI reported Grok 4.6 at #3/49 on Legal Research Bench with 48.1%, 500k context, tool/image/file support, and relatively low pricing. For open models, @ValsAI also highlighted GLM 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 @ZhihuFrontier and summarized by @TheTuringPost frames DeepSeek Harness (DSH) as an intentionally thin shell over a plugin architecture called Cordis. The key design choice is that everything is a plugin, including the agent loop itself. Early beta users reportedly shipped 100+ plugins and filed 400+ issues in under a week; examples range from a gomoku model testbed to a database agent that closes the SQL feedback loop by connecting the model to live query execution. The strongest takeaway is architectural: DSH is less “productized assistant” than open 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 @kimmonismus all covered the launch of TrueForge, 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 both local and hosted deployment modes. The technical claim that resonated: on a 14-task enterprise benchmark, TrueForge matched Claude Managed Agents on Opus 4.8 while using about 30% fewer tokens, and routing to GLM-5.2 cut cost by around 75% while preserving accuracy. The broader industry theme—also echoed by @bradenjhancock and @dbreunig via @rseroter—is that the session/environment/memory/tools layer is becoming a major source of both differentiation and savings.
• Managed harnesses are also getting sharper observability and controls: @ClaudeDevs added memory support for self-hosted sandboxes, domain allow/block controls for web tools, and a redesigned multi-agent session viewer with 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. @OpenAIDevs highlighted the open-source Codex harness as the runtime beneath internal tools, ops dashboards, and custom apps, while @cursor_ai shipped cloud-agent UX improvements around persistent goals and long-lived sessions.

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