Deep Self-Evolving Reasoning (Oct 2025)
Автор: AI Papers Slop
Загружено: 2025-10-24
Просмотров: 95
Title: Deep Self-Evolving Reasoning (Oct 2025)
Link: http://arxiv.org/abs/2510.17498v1
Date: October 2025
Summary:
This paper introduces Deep Self-Evolving Reasoning (DSER), a probabilistic framework that substantially extends the reasoning capabilities of open-weight large language models (LLMs) on complex tasks, even when their inherent verification and refinement abilities are weak. DSER conceptualizes iterative reasoning as a Markov chain, where convergence to a correct solution is assured as long as the probability of improvement marginally exceeds that of degradation. By executing multiple parallel, long-horizon self-evolving processes, DSER amplifies these small positive tendencies to asymptotically approach correct answers. Empirical evaluations on the AIME 2024-2025 benchmark demonstrate that DSER enables the DeepSeek-R1-0528-Qwen3-8B model to solve 5 out of 9 previously unsolvable problems and significantly improve overall performance, even surpassing its 600B-parameter teacher through majority voting. The work also highlights fundamental limitations of current open-weight reasoners and proposes a research agenda for developing intrinsic self-evolving capabilities.
Key Topics:
Deep Self-Evolving Reasoning (DSER)
Large Language Models (LLMs)
Iterative Reasoning
Markov Chain
Self-Verification
Self-Refinement
Olympiad-level Problems
Open-Weight Models
Chain-of-Thought (CoT) Reasoning
Chapters:
00:00 - Small LLM Fragility & DSER
00:54 - DSER's Probabilistic Solution
01:36 - 8B Model Beats 600B
02:28 - Stochastic Reasoning Framework
04:11 - Markov Chains & Convergence
06:08 - DSER Implementation Cycle
07:58 - AIME Benchmark Performance
09:30 - High Cost: 10 Million Tokens
10:47 - Guiding Future AI Development
12:25 - DSER vs. Verification Frameworks
13:11 - DSER's Impact & Research Direction
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