Reward Shaping for Structured Planning and Decision-Making in Large Language Models
Keywords:
reward shaping, large language models, structured planning, decision-making, alignment, governance, infrastructure, robustness, fairnessAbstract
Large language models are increasingly deployed as planners, coordinators, and decision-making agents in complex socio-technical environments. Their capacity to generate structured plans, invoke tools, and adapt to changing constraints depends not only on scale and pretraining but also on how behavior is shaped through reward signals. This paper examines reward shaping as a systems-level mechanism for structured planning and decision-making in large language models. Rather than treating reward design as a narrow optimization detail, the analysis positions reward shaping as an infrastructural and governance layer that intersects with architecture, deployment, monitoring, fairness, robustness, and sustainability. The paper discusses how process rewards, preference models, verifiable outcomes, and trajectory filtering can improve long-horizon planning while introducing new risks such as reward hacking, specification gaming, opacity, and uneven accountability. It further analyzes architectural trade-offs among centralized reward models, decentralized evaluation, tool-augmented planning, and human oversight. Cross-domain illustrations from healthcare, public administration, scientific discovery, and enterprise workflows show that reward-shaped language agents must be evaluated against operational, legal, and ethical criteria rather than benchmark accuracy alone. The paper argues that sustainable deployment requires auditable reward pipelines, participatory governance, robust fallback mechanisms, and lifecycle assessment of compute and data burdens. The conclusion emphasizes that reward shaping for language-based planning should be treated as a socio-technical design problem, not merely an algorithmic one, if such systems are to remain reliable, fair, and legitimate in high-stakes settings.
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