StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions

arXiv:2610.17309 · cs.CL, cs.AI · Submitted 2026-10-12 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-10-12

Updated: 2026-10-20

Comments: Technical report. 38 pages, 16 figures, 15 tables

Code: https://github.com/StartLuxLabs/StartLux-Decision

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• A Survey on LLM-as-a-Judge
• Typed Decision Models: An Early Evidence Audit and Evaluation Checklist
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• Effective Learning for Small Reasoning Models: An Empirical Study on 0.5B Reasoning LLMs
• DAPO: An Open-Source LLM Reinforcement Learning System at Scale
• Kimi k1.5: Scaling Reinforcement Learning with LLMs
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Understanding R1-Zero-Like Training: A Critical Perspective
• Evaluating and Benchmarking the System One Model Jev
• Jev thinks "I don't know'', but doesn't say it: Introducing Sys1Cal-v1 Dataset for Probability Calibration
• Don't Think Twice! Over-Reasoning Impairs Confidence Calibration
• Beyond Calibration: Do a Typed-Decision Model's Probabilities Obey the Probability Axioms?

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