# Reasoning Beyond Limits: Advances and Open Problems for LLMs arXiv: https://arxiv.org/abs/2503.22732 · HTML: https://mlsanity.com/paper/2503.22732/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. - Builds on 123 of its 188 references that are in the map. - First posted on arXiv 26 Mar 2025. ## Placed in - LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/) - Mixture-of-experts / [MoE large language models](https://mlsanity.com/moe/moe-large-language-models/) ## Builds on (most cited first, 30 of 123) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021) - [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024) - [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025) - [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025) - [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024) - [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023) - [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017) - [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024) - [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://mlsanity.com/paper/2503.14476/) (2025) - [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022) - [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024) - [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020) - [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020) - [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025) - [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024) - [Kimi k1.5: Scaling Reinforcement Learning with LLMs](https://mlsanity.com/paper/2501.12599/) (2025) - [OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://mlsanity.com/paper/2402.14008/) (2024) - [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024) - [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://mlsanity.com/paper/2411.15124/) (2024) - [Large Language Monkeys: Scaling Inference Compute with Repeated Sampling](https://mlsanity.com/paper/2407.21787/) (2024) - [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://mlsanity.com/paper/2405.14734/) (2024) - [Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://mlsanity.com/paper/2312.08935/) (2023) - [Solving math word problems with process- and outcome-based feedback](https://mlsanity.com/paper/2211.14275/) (2022) - [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024) - [Training Large Language Models to Reason in a Continuous Latent Space](https://mlsanity.com/paper/2412.06769/) (2024) - [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024) - [SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training](https://mlsanity.com/paper/2501.17161/) (2025) ## Built on by (most cited first, 16 of 16) - [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025) - [WebDancer: Towards Autonomous Information Seeking Agency](https://mlsanity.com/paper/2505.22648/) (2025) - [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025) - [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025) - [From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows](https://mlsanity.com/paper/2506.23260/) (2025) - [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026) - [α3-Bench: A Unified Benchmark of Safety, Robustness, and Efficiency for LLM-Based UAV Agents over 6G Networks](https://mlsanity.com/paper/2601.03281/) (2026) - [PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions](https://mlsanity.com/paper/2505.15472/) (2025) - [From Emergence to Control: Probing and Modulating Self-Reflection in Language Models](https://mlsanity.com/paper/2506.12217/) (2025) - [Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMs](https://mlsanity.com/paper/2507.21193/) (2025) - [Out-of-distribution Tests Reveal Compositionality in Chess Transformers](https://mlsanity.com/paper/2510.20783/) (2025) - [Tool-Augmented Hybrid Ensemble Reasoning with Distillation for Bilingual Mathematical Problem Solving](https://mlsanity.com/paper/2512.19093/) (2025) - [SciTaRC: A Plan-Annotated Scientific Tabular QA Benchmark for Language Reasoning and Complex Computation](https://mlsanity.com/paper/2603.08910/) (2026) - [TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models](https://mlsanity.com/paper/2605.12518/) (2026) - [Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions](https://mlsanity.com/paper/2607.03233/) (2026) - [PhysAI-Bench: A Benchmark for LLM-Based Agentic Decision-Making in Autonomous UAV-Centric Physical AI](https://mlsanity.com/paper/2609.23695/) (2026) ## Abstract (arXiv) Recent breakthroughs in generative reasoning have fundamentally reshaped how large language models (LLMs) address complex tasks, enabling them to dynamically retrieve, refine, and organize information into coherent multi-step reasoning chains. Techniques such as inference-time scaling, reinforcement learning, supervised fine-tuning, and distillation have been effectively applied to state-of-the-art models, including DeepSeek-R1, OpenAI o1 and o3, GPT-4o, Qwen-32B, and various Llama variants, significantly enhancing their reasoning capabilities. In this paper, we present a comprehensive review of the top 27 LLMs released between 2023 and 2025, such as Mistral AI Small 3 24B, DeepSeek-R1, Search-o1, QwQ-32B, and Phi-4, and analyze their core innovations and performance improvements. We also provide a detailed overview of recent advancements in multilingual large language models (MLLMs), emphasizing methods that improve cross-lingual reasoning and address the limitations of English-centric training. In parallel, we present a comprehensive review of progress in state space model (SSM)-based architectures, including models such as Mamba, which demonstrate improved efficiency for long-context processing compared to transformer-based approaches. Our analysis covers training strategies including general optimization techniques, mixture-of-experts (MoE) configurations, retrieval-augmented generation (RAG), chain-of-thought prompting, self-improvement methods, and test-time compute scaling and distillation frameworks. Finally, we identify key challenges for future research, including enabling multi-step reasoning without human supervision, improving robustness in chained task execution, balancing structured prompting with generative flexibility, and enhancing the integration of long-context retrieval and external tools.