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#reasoning benchmarks

Self-Hinting Language Models Enhance Reinforcement Learning

Intermediate

Baohao Liao, Hanze Dong et al.Feb 3arXiv

When rewards are rare, a popular training method for language models (GRPO) often stops learning because every try in a group gets the same score, so there is nothing to compare.

#reinforcement learning#GRPO#self-hinting

TTCS: Test-Time Curriculum Synthesis for Self-Evolving

Intermediate

Chengyi Yang, Zhishang Xiang et al.Jan 30arXiv

TTCS is a way for a model to teach itself during the test by first making easier practice questions that are similar to the real hard question and then learning from them.

#test-time training#test-time reinforcement learning#curriculum learning

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment

Intermediate

Yuming Yang, Mingyoung Lai et al.Jan 20arXiv

The paper asks a simple question: Which step-by-step explanations from a teacher model actually help a student model learn to reason better?

#Rank-Surprisal Ratio#data-student suitability#chain-of-thought distillation

Evaluating Parameter Efficient Methods for RLVR

Intermediate

Qingyu Yin, Yulun Wu et al.Dec 29arXiv

The paper asks which small, add-on training tricks (PEFT) work best when we teach language models with yes/no rewards we can check (RLVR).

#RLVR#parameter-efficient fine-tuning#LoRA