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GSM8K

Papers

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Showing 251–275 of 439 papers

TitleStatusHype
Upweighting Easy Samples in Fine-Tuning Mitigates ForgettingCode0
Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference—0
Pheromone-based Learning of Optimal Reasoning Paths—0
RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations—0
Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs—0
DNA 1.0 Technical Report—0
ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem SolvingCode0
DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy TheoryCode0
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models—0
InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion—0
Recursive Decomposition of Logical Thoughts: Framework for Superior Reasoning and Knowledge Propagation in Large Language Models—0
DIVE: Diversified Iterative Self-ImprovementCode0
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs—0
LLM2: Let Large Language Models Harness System 2 ReasoningCode0
Towards Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search Boosted Reasoning via Iterative Preference Learning—0
System-2 Mathematical Reasoning via Enriched Instruction Tuning—0
Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions—0
Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-SolvingCode0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Falcon: Faster and Parallel Inference of Large Language Models through Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree—0
A Graph-Based Synthetic Data Pipeline for Scaling High-Quality Reasoning Instructions—0
Learning to Reason via Self-Iterative Process Feedback for Small Language Models—0
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1XolverAccuracy98.1—Unverified
2Orange-mini0-shot MRR98—Unverified