Papers2

#Reward Sparsity

Learn Hard Problems During RL with Reference Guided Fine-tuning

ReGFT is a simple pre-RL step that shows the model partial human hints, then makes it solve problems in its own words, creating correct, model-style solutions for hard questions.

#Reference-Guided Fine-Tuning#ReGFT#ReFT

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Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO

Intermediate

Yunze Tong, Mushui Liu et al.Feb 6arXiv

Text-to-image models using GRPO used to give the same final reward to every step, which is like giving the whole team the same grade no matter who did what.

#TurningPoint-GRPO#GRPO#Flow Matching

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