post-training

This paper investigates offline reinforcement learning for post-training code-generating LLMs, showing that it can improve zero-shot code generation performance using existing datasets without online sampling.

This study investigates off-target effects of response-style alignment in a Korean 27B language model, finding that post-training for style significantly impacts answer propensity and disclosure rates without targeting safety or capability.

LOCUS is a task-aware low-rank post-training method that reduces output token length in language models while maintaining preference alignment, achieving up to 39.84% reduction on Pythia-2.8B with minimal parameter updates.

Nebius has launched the AI Builder Program, offering AI builders resources such as runnable examples, blueprints, courses, and over $400 in credits to facilitate building AI systems.

This paper introduces a data-centric pipeline for post-training language models to enhance financial reasoning through mining reasoning traces, distilling instruction data, and generating verifiable QA pairs, demonstrating improvements in performance while preventing catastrophic forgetting.

Direct Diversity Optimization (DDO) is an offline post-training method that improves successful strategy coverage in LLM agents for sequential decision tasks, outperforming other methods in benchmarks like BabyAI, BabaIsAI, and WebShop.

The paper proposes SALT, a lightweight post-training method that injects span-level supervision into cross-lingual sentence encoders to improve token representations, achieving top results on multilingual token-level benchmarks and enhancing sentence-level performance.

ActReview is a rebuttal-guided post-training framework that generates diagnostic claims and revision suggestions for peer reviews by leveraging author responses as supervision, along with a human-curated benchmark for evaluation.

Miles v0.1 is an open-source, production-ready system for large-scale reinforcement learning and post-training, supporting diverse backends and models like GLM-5.2, with a focus on scalability and accessibility.

This repository provides GGUF quantizations of the Qwen3.8-Flash-Next model using gradient-based methods GSQ and RCO for optimized low-bit representation, enabling efficient deployment in standard tools.

This paper finds that large language models can gain reasoning improvements from truncated reasoning trajectories rather than full ones during post-training, reducing redundancy while benefiting methods like supervised fine-tuning and reinforcement learning.

MovieGrid is a multi-grid post-training paradigm that decomposes long videos into spatially arranged chunks to improve multi-shot coherence and efficiency, achieving state-of-the-art intra-shot and inter-shot consistency in video generation.

Radixark shares a blog post about how Miles supports multimodal learning for AI models with a shared post-training design for vision-language models and diffusion models.

Arize Phoenix now supports GLM-5.3, which builds on GLM-5.2 with improvements from post-training and training on scaled long-horizon environments using an open-source reinforcement learning framework.

XMerge introduces a post-training method for compressing transformer-based LLMs by merging layers through cross-axis selection and reconstructive reconstruction, outperforming existing methods at aggressive depth reduction while maintaining performance.

Z.AI releases GLM-5.3 Fast, an advanced open-weight AI model optimized for agentic coding and cybersecurity, featuring a 744B-A40B MoE architecture with substantial benchmark improvements.

Fal.ai launched H3 Max, a post-trained variant of MiniMax H3 optimized for high-quality video generation. It claims top rankings in prompt adherence, aesthetics, and speed—generating 5-second videos in ~3 seconds with 35× the throughput of the official H3 model.

Extended reasoning and post-training are key techniques for enhancing AI model performance, with speculation that future models like Qwen 4 could match or surpass large parameter models on specific tasks.

The article argues that base AI models are no longer the primary bottleneck, with improvements now driven by post-training enhancements as seen in recent releases like GLM5.3 and Qwen3.6.

This paper introduces World Model RL to scale automatic research agents by replacing environment execution with a learned world model, thereby accelerating post-training by 3-4x and enabling smaller agents to outperform larger ones on benchmarks.