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#LLM fine-tuning

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#LLM fine-tuning18/10/2025

Sigmoid Scaling Lets Teams Predict RL Post-Training Returns for LLMs

'New research shows RL post-training progress follows sigmoid compute-performance curves and presents ScaleRL, a recipe validated up to 100k GPU-hours for predictable scaling of LLMs.'

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#LLM fine-tuning03/10/2025

Tinker from Thinking Machines: Low-Level API for Distributed LoRA Training

'Tinker is a Python API that exposes low-level training primitives so you can run custom loops locally while the platform handles distributed execution; it focuses on LoRA adapters, portable weights, and managed GPU clusters.'

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#LLM fine-tuning14/09/2025

Build AI Fast: Top 5 No-Code Platforms for Engineers and Developers

'Explore five no-code platforms that simplify building AI assistants, RAG systems, model tuning, and agent workflows in minutes'

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#LLM fine-tuning24/08/2025

Prefix-RFT: Guiding LLMs with Partial Demonstrations to Merge SFT and RFT

Prefix-RFT blends supervised and reinforcement fine-tuning by using partial demonstration prefixes to guide exploration, achieving stronger and more stable performance on math reasoning benchmarks than SFT, RFT, and hybrid baselines.

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#LLM fine-tuning10/08/2025

From 100K to Under 500: Google’s Active Learning Cuts LLM Training Data by Orders of Magnitude

'Google shows active learning can fine-tune LLMs with under 500 targeted labels, matching or improving model quality while reducing labeling needs by orders of magnitude.'

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