Z.ai debuts GLM-5.3, using the same base model as GLM-5.2 with scaled post-training for stronger coding and cyber skills, with weights due in two weeks (Z.ai)
Z.ai : Z.ai debuts GLM-5.3, using the same base model as GLM-5.2 with scaled post-training for stronger coding and cyber skills, with weights due in two weeks With GLM-5.2 we built the stack: IndexSha
At a glance
- techmeme.com: Z.ai debuts GLM-5.3, using the same base model as GLM-5.2 with scaled post-training for stronger coding and cyber skills, with weights due in two weeks (Z.ai)
- arxiv.org: Post-Training with Policy Gradients: Optimality and the Base Model Barrier
The story
techmeme.com: Z.ai : Z.ai debuts GLM-5.3, using the same base model as GLM-5.2 with scaled post-training for stronger coding and cyber skills, with weights due in two weeks With GLM-5.2 we built the stack: IndexShare for efficient long-context processing, SAO for RL on long-horizon tasks
arxiv.org: arXiv:2603.06957v2 Announce Type: replace-cross Abstract: We study post-training linear autoregressive models with outcome and process rewards. Given a context $\boldsymbol{x}$, the model must predict the response $\boldsymbol{y} \in Y^N$, a sequence of length $N$ that satisfies a $\gamma$ margin condition, an extension of the standard separability to sequences. We prove that on test samples where the base model achieves a non-trivial likelihood $\alpha$, a variant of policy gradient (PG) can achieve likelihood $1 - \varepsilon$ with an essentially minimax optimal number of reward queries $\tilde{O}((\alpha^{-1} + \varepsilon^{-1})/\gamma^2)$. However, a barrier arises for going beyond the support of the base model. We prove that the overall expected error after post-training with outcome rewards is governed by a property of the base model called the Likelihood Quantile (LQ), and that variants of PG, while minimax optimal, may require a number of reward queries exponential in $N$ to go beyond this support, regardless of the pre-training algorithm. To overcome this barrier, we study post-training with a process reward model, and demonstrate how PG variants in this setting avoid the curse of dimensionality in $N$ via dependence on a token-level LQ. Along the way, we prove that under the margin condition, SGD with adaptive learning rate (LR) achieves a near optimal test error for statistical learning, and PG with adaptive LR achieves a near optimal number of mistakes for online learning while being computationally efficient whenever possible, both of which may be of independent interest.