Efficient Chain-of-Thought Distillation for Compact Language Models under Limited Computational Budgets

Abstract

Chain-of-thought prompting has become a central technique for eliciting multi-step reasoning in large language models, but its computational demands make direct deployment impractical for many organizations, edge environments, and budget-constrained research settings. This paper examines efficient chain-of-thought distillation for compact language models under limited computational budgets. Rather than treating distillation as a purely algorithmic compression problem, the analysis frames it as a socio-technical systems challenge involving teacher trajectory generation, data filtering, student capacity allocation, training infrastructure, evaluation, governance, and lifecycle sustainability. The paper argues that effective distillation depends less on transferring verbose rationales verbatim than on selecting, filtering, and aligning reasoning traces so that compact students acquire transferable procedural competence. It further examines structural trade-offs among teacher scale, trajectory quality, student architecture, quantization, adapter-based tuning, inference latency, and evaluation rigor. Particular attention is given to robustness, fairness, and policy implications, because compact reasoning models may be deployed in high-stakes settings where opaque failure modes and biased supervision can produce uneven harm. The paper concludes that efficient chain-of-thought distillation should be designed as a governed pipeline rather than a single training run, with explicit attention to data provenance, evaluation validity, deployment constraints, and environmental cost. Such a pipeline can widen access to reasoning-capable models while preserving accountability, but only if efficiency gains are matched by system-level transparency and continuous monitoring.