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A Survey of Reasoning with Foundation Models
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A Survey of Reasoning with Foundation Models
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Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation models, e.g., Large Language Models (LLMs), there is a growing interest in exploring their abilities in reasoning tasks. In this paper, we introduce seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models. We also discuss the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning. By discussing these future research directions, we hope to inspire researchers in their exploration of this field, stimulate further advancements in reasoning with foundation models, and contribute to the development of AGI.
Forward citations
Cited by 9 Pith papers
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Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning
Rewarding each parallel reasoning path by Monte-Carlo-Shapley marginal contribution, scored by a generative reward model, lifts Pass@16 on AIME24/AIME25/AMC23 by 4-90% relative over Parallel-R1 with a fifth of the tra...
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The Shape of Reasoning: Topological Analysis of Reasoning Traces in Large Language Models
A TDA-based framework is proposed to assess LLM reasoning trace quality, showing that topological features predict quality better than standard graph metrics.
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The Shape of Reasoning: Topological Analysis of Reasoning Traces in Large Language Models
Topological features of reasoning-trace embeddings correlate with Smith-Waterman alignment to expert AIME solutions more than graph metrics do, but the paper does not validate this out of sample.
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MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Bootstrapping math questions via rewriting creates MetaMathQA; fine-tuning LLaMA-2 on it yields 66.4% on GSM8K for 7B and 82.3% for 70B, beating prior same-size models by large margins.
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CoVeR: Conformal Calibration for Versatile and Reliable Autoregressive Next-Token Prediction
CoVeR is a cluster-aware conformal decoding method that claims full-sequence coverage for LLM outputs without the (1-alpha)^L decay of prior conformal beam search.
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Implicit Reasoning in Large Language Models: A Comprehensive Survey
A survey organizing implicit (silent) reasoning in LLMs into three execution paradigms, plus evidence, benchmarks, and challenges.
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Learning from Diverse Reasoning Paths with Routing and Collaboration
QR-Distill filters, routes, and collaboratively distills multiple teacher reasoning paths into two 7B student models, but its superiority claims are weakened by unfair baselines and contradictory ablations.
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Understanding the planning of LLM agents: A survey
A survey that provides a taxonomy of methods for improving planning in LLM-based agents across task decomposition, plan selection, external modules, reflection, and memory.
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Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
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