arXiv (All)
open access
Fine-Tuning Small Reasoning Models for Quantum Field Theory
Despite the growing application of large language models (LLMs) to theoretical physics, there has been little academic exploration of how domain-specific physics reasoning ability develops during training. To investigate this, we perform the first academic comparison of reinforcement learning and su
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- What is the Role of Small Models in the LLM Era: A Survey#1049587
- CME-SpectrumBench: Can LLMs Analyze Condensed Matter Spectral Data?#1388870
- Understanding When Reflective Prompt and Skill Optimization Helps on Locally Served Reasoning Models: A Two-Model Study of GEPA and SkillOpt#1401205
- Beyond Dense States: Sparse Transcoders as Causally Testable Operators for LLM Latent Reasoning#793566
- Beyond Dense States: Sparse Transcoders as Causally Testable Operators for LLM Latent Reasoning#789562
Record · ID 1412803
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive