Three Roles, One Model: Role Orchestration at Inference Time to Close the Performance Gap Between Small and Large Agents

Large language model (LLM) agents show promise on realistic tool-use tasks, but deploying capable agents on modest hardware remains challenging. We study whether inference-time scaffolding alone, without any additional training compute, can improve the performance of a small model in complex multi-step environments. Operating on a single 24\,GB GPU, we evaluate Qwen3-8B under both full-precision (FP16, 12K context) and 4-bit quantized (AWQ, 32K context) configurations. Without any intervention, the raw model achieves just 5.4\% (FP16) and 3.0\% (AWQ) task goal completion. Guided by a systematic failure mode analysis, we introduce a three-tier inference scaffolding pipeline that deploys the same frozen model in three distinct roles: (1) a summarization model that preserves critical artifacts (tokens, credentials, API responses) while compressing dialogue history; (2) the main agent model that reasons over the compressed context; and (3) an isolated correction model that reviews and revises the agent's code output without access to conversation history, breaking repetitive failure loops. Applied to the same unmodified model, this scaffolding yields 8.9\% (FP16) and 5.9\% (AWQ) task goal completion, roughly doubling performance in both settings, with particularly strong gains on difficulty-1 tasks (15.8\%$\to$26.3\% FP16; 5.3\%$\to$14.0\% AWQ). On full-precision inference, our scaffolded 8B model surpasses DeepSeek-Coder 33B Instruct (7.1\%) from the original AppWorld evaluation, demonstrating that structured inference-time interventions can make small models competitive with systems 4$\times$ their size. We formalize the approach as a scaffolded policy over a frozen base model, three invocations of the same weights with different conditioning, drawing connections to test-time compute scaling and action-space shaping in reinforcement learning.

Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments

In modern ML Ops environments, model deployment is a critical process that traditionally relies on static heuristics such as validation error comparisons and A/B testing. However, these methods require human intervention to adapt to real-world deployment challenges, such as model drift or unexpected performance degradation. We investigate whether reinforcement learning, specifically multi-armed bandit (MAB) algorithms, can dynamically manage model deployment decisions more effectively. Our approach enables more adaptive production environments by continuously evaluating deployed models and rolling back underperforming ones in real-time. We test six model selection strategies across two real-world datasets and find that RL based approaches match or exceed traditional methods in performance. Our findings suggest that reinforcement learning (RL)-based model management can improve automation, reduce reliance on manual interventions, and mitigate risks associated with post-deployment model failures.