Fine Tuning + Training + Reasoning | hypedar

Fine Tuning + Training + Reasoning

23.0

Implement the Memory Dial framework for fine-tuning open models. This allows developers to explicitly control what data is memorized by the weights versus generalized.

+0

emergingimplementation gap

trainingrlreasoningfine-tuningslm

Signals (20)

CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data

Cross-Lingual Transfer and Parameter-Efficient Adaptation in the Turkic Language Family: A Theoretical Framework for Low-Resource Language Models

The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment

LLM Reasoning with Process Rewards for Outcome-Guided Steps

Cross-Tokenizer LLM Distillation through a Byte-Level Interface

Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability

LLM-Augmented Knowledge Base Construction For Root Cause Analysis

Self-Execution Simulation Improves Coding Models

Revealing the Learning Dynamics of Long-Context Continual Pre-training

NVIDIA-NeMo/DataDesigner

Understanding the Nature of Generative AI as Threshold Logic in High-Dimensional Space

Google AI17h ago

ConvApparel: Measuring and bridging the realism gap in user simulators

Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk Modeling

Reinforcement Learning-based Knowledge Distillation with LLM-as-a-Judge

Limits of Difficulty Scaling: Hard Samples Yield Diminishing Returns in GRPO-Tuned SLMs

Consistency-Guided Decoding with Proof-Driven Disambiguation for Three-Way Logical Question Answering

Memory Dial: A Training Framework for Controllable Memorization in Language Models

Beyond Facts: Benchmarking Distributional Reading Comprehension in Large Language Models

shiyu-coder/Kronos