supervised fine-tuning (SFT)

2026-11-02

CODING · 1 SOURCE · AWS Machine Learning

Amazon SageMaker AI guide: sequence-level knowledge distillation for efficient agents

Amazon’s SageMaker AI blog post explains how to use sequence-level knowledge distillation (SeqKD) to train smaller, cheaper ‘student’ models that reproduce the multi-step behavior of larger frontier ‘teacher’ models. The post covers generating high-quality agentic trajectories via a teacher API, filtering solved runs with a programmatic scorer, encoding them for supervised fine-tuning, running serverless training, and evaluating the student on a held-out benchmark.

6.0

2026-10-27

COMPANIES · 1 SOURCE · AWS Machine Learning

Exowatt uses SageMaker serverless customization to fine-tune Qwen3 models for edge and cloud, beating frontier models

Exowatt built a repeatable pipeline on Amazon SageMaker AI serverless model customization to produce task-specific fine-tuned models for edge alarm triage (Qwen3-1.7B) and cloud energy-market analysis (Qwen3-14B). The tuned models outperformed benchmark frontier models on accuracy for these tasks and reached up to 49× lower cost, while enabling a repeatable path from raw operational data to deployable artifacts in days rather than months.

6.0

2026-10-12

RESEARCH · 1 SOURCE · arXiv cs.AI

Uncertainty-Guided Progressive Contraction for efficient reasoning post-training (arXiv:2610.12933v1)

The paper proposes an uncertainty-guided progressive contraction framework that compresses reasoning supervision and focuses optimization on high-value reasoning trajectories. It builds Monte Carlo reasoning trees around intermediate CoT states to profile uncertainty, first contracts a teacher-informed training pool and then performs learner-aware online routing during SFT, claiming up to ~450x less training exposure (as little as 0.22% of full-data SFT) while maintaining or improving reasoning across benchmarks and diverse LLM families.

7.0

2026-10-07

MODELS · 1 SOURCE · Hugging Face

Nemotron-3 specialists fine-tuned to gold level at IOI 2026 and IMO 2026

Teams starting from Nemotron 3 used supervised fine-tuning, reinforcement learning, and an inference loop (GenCorrect) to create specialist checkpoints. Nemotron-3-Ultra-CC scored 535.4/600 in a live IOI 2026 run (unofficial benchmark) and a combined IMO system scored 30/42 on IMO 2026 proofs judged by official graders, exceeding the gold threshold.

8.0

2026-10-02

MODELS · 1 SOURCE · Hugging Face

Asta open-sources AstaBrief 8B, a fast cited-report model

Asta released AstaBrief 8B, an 8-billion-parameter model that generates cited scientific reports from a research question and retrieved literature excerpts; it is deployed in Asta’s Generate a report feature as Fast mode alongside Claude-powered Thinking mode and the team is open-sourcing the model weights, training data, and an example local workflow. AstaBrief—built from Qwen3-8B with SFT and DPO-focused training data—produces reports about 3.5× faster in Asta’s pipeline (51.1s vs 178.5s) and was mostly trained and evaluated in 2025.

7.0

2026-09-21

RESEARCH · 1 SOURCE · arXiv cs.AI

LogicTrack: auditing LLM reasoning with formal logic solvers

LogicTrack is a neuro-symbolic framework that auto-formalizes each Chain-of-Thought step and verifies them with automated theorem provers, introducing a Solver-Based Backtracking Reward (SBR) to score step-wise logical soundness and guide backtracking tree search at inference time. The authors also use backtracking traces to build supervised fine-tuning data and report improved reasoning-chain verifiability and final-answer pass rates across 8 benchmarks and 7 LLMs (arXiv:2609.21492v1).

6.0

2026-09-15

MODELS · 1 SOURCE · AWS Machine Learning

Walkthrough: Customize Qwen3-8B on SageMaker serverless to build an AI product-tagging system

AWS Machine Learning published a walkthrough that demonstrates customizing Qwen3-8B using supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) via Amazon SageMaker serverless model customization, then deploying it for asynchronous inference to create a cost-efficient product tagging system.

5.0