sft

KAT-Coder-V2.5-Dev is an open-weight MoE coding model with 35B total parameters (3B active), achieving state-of-the-art results on agentic coding benchmarks through SFT and RL training.

An open-source end-to-end post-training recipe covering SFT, RL, and test-time scaling with models, datasets, and code.

KG2Cypher presents a data-centric pipeline for building enterprise text-to-Cypher systems from existing knowledge graphs. It uses LLMs to generate natural language question-Cypher pairs, validated by an LLM judge and human review, and achieves significant performance improvements on Korean enterprise datasets with LoRA-based fine-tuning.

A researcher describes a method called 'projection' to fine-tune AI agents by projecting a verifiably correct solution onto a map the agent can traverse, achieving improved performance on cybersecurity tasks with limited training data.

Fara1.5 is a family of native computer use agents trained using the FaraGen1.5 scalable data pipeline. The models achieve new state-of-the-art results on browser-use benchmarks, competing with much larger frontier models.

DataClaw0 proposes an agentic data tailoring paradigm that uses learnable data processing to structure high-entropy multimodal streams, achieving robust alignment via SFT and GRPO on a novel benchmark.

The thread shares systematic experimental findings on fine-tuning best practices, varying one SFT lever at a time across dense and MoE models up to 235B on four real-world customer datasets with custom evals to eliminate confounders.

A detailed tutorial on supervised fine-tuning (SFT) for training AI agents, built from scratch in pure PyTorch using Qwen3-0.6B, explaining the mechanics of next-token prediction and label masking.

An open-source LLM called OpenMythos was trained for cybersecurity tasks using SFT and RLVR, with datasets available on HuggingFace. The model aims to reduce hallucinations and improve precision in security-related queries.

Microsoft released FastContext-1.0, a lightweight repository-exploration subagent for LLM coding agents that reduces main-agent token consumption by up to 60% while improving resolution rates by up to 5.5%.

The tweet outlines a 3-step loop for LLM training in 2026: train on data, run evals, and add synthetic data for underperforming tasks. It emphasizes the accessibility of legal distillation via open source models and cheap APIs, noting that training on reasoning traces alone can achieve high scores.

TRL now supports fine-tuning models on agent traces from various sources like Claude Code, Codex, OpenClaw, and Pi, moving towards a standardized stack for training agentic models.

OpenBMB released UltraData-SFT-2605, a 15M-sample high-quality SFT dataset for fine-tuning AI models like MiniCPM5-1B to run on phones or laptops.

OpenBMB releases UltraData-SFT-2605, a large-scale dataset with over 15 million high-quality samples for supervised fine-tuning (SFT) of reasoning LLMs, covering deep thinking, non-thinking, math, code, knowledge, instruction following, and multilingual data.

We propose LIFT, a learnability-informed fine-tuning algorithm for diffusion language models that aligns training with token difficulty and time step, achieving substantial gains on reasoning benchmarks.

An experiment comparing three Supervised Fine-Tuning data formats (demonstrations, first-person statements, synthetic documents) for injecting a C-3PO persona into Qwen3-4B, finding first-person statements best for generalization and synthetic documents best for factual knowledge.

Dynamic Fine-Tuning (DFT) is introduced as a method that reweights the SFT loss using the model's own token probability, creating a feedback loop, and adds forward KL to penalize tokens the base model finds likely but the policy has pushed toward zero probability. The tweet expresses skepticism about SFT papers in practice but praises the attempt.

This article describes using Fireworks Agent to automate the fine-tuning of a small open-weight model to generate wiki-style summaries, enabling a self-improving agent loop where model training becomes a callable step.

Anyscale introduces a new Agent Skill for LLM post-training that automatically selects the optimal fine-tuning method (SFT, DPO, GRPO, etc.) and generates ready-to-launch configs, helping avoid wasted GPU runs.

The author trained 1B, 2B, and 3B models with the same SFT recipe and observed that instruction-following (IFEval) regressed for the 1B and 2B models but improved for the 3B, possibly due to different learning rates or model capacity.