Reinforcement Learning for Temporal Reasoning and Sequential Decision-Making in Language Models

Abstract

Reinforcement learning has become a central mechanism for aligning large language models with complex objectives, yet its role in temporal reasoning and sequential decision-making remains under-theorized from a systems perspective. This paper examines how reinforcement learning architectures, reward modeling pipelines, inference-time control, and deployment infrastructures interact when language models are asked to reason across time, plan over multiple steps, and act within partially observable environments. It argues that temporal reasoning in language models is not merely a prompt-engineering problem but a socio-technical capability shaped by credit assignment, trajectory filtering, memory design, latency budgets, governance structures, and evaluation regimes. The discussion compares policy-gradient methods, preference optimization, verbal reinforcement, and tool-augmented agent loops, highlighting trade-offs among sample efficiency, interpretability, robustness, and computational sustainability. Special attention is given to small-parameter models, where scalable reasoning path filtering and alignment can reduce training costs but may also constrain representational capacity. The paper further considers fairness, safety, and policy implications when sequential language agents mediate decisions in education, healthcare, finance, and public administration. It concludes that responsible temporal reasoning requires layered governance, reproducible evaluation, energy-aware infrastructure, and institutions capable of auditing multi-step behavior rather than isolated outputs.