Large Language Models' Reasoning Stalls: An Investigation into the Capabilities of Frontier Models

• URL: http://arxiv.org/abs/2505.19676v2
• Date: Tue, 17 Jun 2025 01:49:49 GMT
• Title: Large Language Models' Reasoning Stalls: An Investigation into the Capabilities of Frontier Models
• Authors: Lachlan McGinness, Peter Baumgartner,
• Abstract summary: We evaluate the performance of State of the Art models from December 2023 and August 2024 on PRONTOQA steamroller reasoning problems.<n>Our results show that progress in improving LLM reasoning abilities has stalled over the nine month period.
• Score: 0.18416014644193066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Empirical methods to examine the capability of Large Language Models (LLMs) to use Automated Theorem Prover (ATP) reasoning strategies are studied. We evaluate the performance of State of the Art models from December 2023 and August 2024 on PRONTOQA steamroller reasoning problems. For that, we develop methods for assessing LLM response accuracy and correct answer correlation. Our results show that progress in improving LLM reasoning abilities has stalled over the nine month period. By tracking completion tokens, we show that almost all improvement in reasoning ability since GPT-4 was released can be attributed to either hidden system prompts or the training of models to automatically use generic Chain of Thought prompting strategies. Among the ATP reasoning strategies tried, we found that current frontier LLMs are best able to follow the bottom-up (also known as forward-chaining) strategy. A low positive correlation was found between an LLM response containing correct reasoning and arriving at the correct conclusion.

Related papers

• ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context [66.15505423059234]
  We introduce ASTRO, a framework for training language models to reason like search algorithms.<n>We apply ASTRO to the Llama 3 family of models and achieve absolute performance gains of 16.4% on MATH-500, 26.9% on AMC 2023, and 20.0% on AIME 2024.
  arXiv  Detail & Related papers  (2025-07-01T04:10:15Z)
• Interleaved Reasoning for Large Language Models via Reinforcement Learning [22.403928213802036]
  Long chain-of-thought (CoT) enhances large language models' (LLM) reasoning capabilities.<n>We propose a novel training paradigm that uses reinforcement learning (RL) to guide reasoning LLMs to interleave thinking and answering for multi-hop questions.
  arXiv  Detail & Related papers  (2025-05-26T07:58:17Z)
• KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation [78.96590724864606]
  We introduce the Knowledge Orthogonal Reasoning Gymnasium (KORGym), a dynamic evaluation platform inspired by KOR-Bench and Gymnasium.<n>KORGym offers over fifty games in either textual or visual formats and supports interactive, multi-turn assessments with reinforcement learning scenarios.
  arXiv  Detail & Related papers  (2025-05-20T16:06:32Z)
• Think Deep, Think Fast: Investigating Efficiency of Verifier-free Inference-time-scaling Methods [39.89239733570008]
  This work conducts a comprehensive analysis of inference-time scaling methods for both reasoning and non-reasoning models.<n>We find that non-reasoning models, even with an extremely high inference budget, still fall substantially behind reasoning models.<n>For reasoning models, majority voting proves to be a robust inference strategy, generally competitive or outperforming other more sophisticated ITC methods.
  arXiv  Detail & Related papers  (2025-04-18T19:32:55Z)
• Towards Reasoning Ability of Small Language Models [3.732224317444325]
  We show that small language models (SLMs) can achieve competitive reasoning performance.<n>We systematically survey, benchmark, and analyze 72 SLMs from six model families across 14 reasoning benchmarks.<n>Our findings challenge the assumption that scaling is the only way to achieve strong reasoning.
  arXiv  Detail & Related papers  (2025-02-17T08:59:16Z)
• Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding [74.31981011985681]
  Large language models (LLMs) have shown impressive capabilities, but still struggle with complex reasoning tasks requiring multiple steps. We introduce LaTent Reasoning Optimization (LaTRO), a principled framework that formulates reasoning as sampling from a latent distribution. We validate LaTRO through experiments on GSM8K and ARC-Challenge datasets using multiple model architectures.
  arXiv  Detail & Related papers  (2024-11-06T22:02:30Z)
• Steamroller Problems: An Evaluation of LLM Reasoning Capability with Automated Theorem Prover Strategies [0.18416014644193066]
  We evaluate the performance of GPT4, GPT3.5 Turbo and Google's recent Gemini model on problems from a steamroller domain. We found that the models' performance when using the ATP reasoning strategies was comparable to one-shot chain of thought.
  arXiv  Detail & Related papers  (2024-07-17T22:49:23Z)
• LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models [63.14196038655506]
  We introduce LogicAsker, a novel approach for evaluating and enhancing the logical reasoning capabilities of large language models (LLMs) Our methodology reveals significant gaps in LLMs' learning of logical rules, with identified reasoning failures ranging from 29% to 90% across different models. We leverage these findings to construct targeted demonstration examples and fine-tune data, notably enhancing logical reasoning in models like GPT-4o by up to 5%.
  arXiv  Detail & Related papers  (2024-01-01T13:53:53Z)
• A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning [73.77088902676306]
  We take a closer look at the self-verification abilities of large language models (LLMs) in the context of logical reasoning. Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods.
  arXiv  Detail & Related papers  (2023-11-14T07:13:10Z)
• Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals [67.64770842323966]
  Causal explanations of predictions of NLP systems are essential to ensure safety and establish trust. Existing methods often fall short of explaining model predictions effectively or efficiently. We propose two approaches for counterfactual (CF) approximation.
  arXiv  Detail & Related papers  (2023-10-01T07:31:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.