Towards Robust Reinforcement Learning for Small-Scale Language Model Agents
The alignment of Small Language Models (SLMs) in the 70--500M parameter range using reinforcement learning is often considered unstable, though the underlying failure mechanisms have not been systematically investigated. In the State-of-the-Art (SOTA) research, fifteen (model, corpus) configurations were trained using Proximal Policy Optimization (PPO). The experiments included Pythia-70M, 160M, 410M and SmolLM2-135M, 360M on the TinyStories, CNN/DailyMail, and Wikitext-103 corpora. Three reprod
Record details
Published: 27 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Agents & autonomy · Finance, VC & PE
Retrieved: 29 July 2026
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ethics.ai (27 July 2026), “Towards Robust Reinforcement Learning for Small-Scale Language Model Agents,” evidence record 14166, https://ethics.ai/record/14166 (originally published by arXiv).
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