Ralph 1.5 Think

Ralph 1.5 Think is an experimental research model exploring whether a small language model can be fine-tuned to exhibit reasoning capabilities that outperform its non-thinking base model and compete with significantly larger models.

The model is built by fine-tuning Qwen2-1.5B from Alibaba, transforming a standard SLM into a reasoning-oriented variant through targeted supervision and training strategies. The goal is not scale, but efficiency: extracting maximal reasoning performance from minimal parameters.

This work specifically investigates:

• Whether explicit reasoning fine-tuning can meaningfully elevate SLM performance.
• How far a 1.5B parameter model can be pushed against multi-billion-parameter baselines.
• The trade-offs between model size, reasoning depth, and data efficiency.

Benchmarks (Current)

GSM8K

• 50 random samples: 42% accuracy
• 100 random samples: 45% accuracy

Notes:

• Samples are randomly selected.
• Evaluation is performed on local infrastructure.
• Results are preliminary and expected to improve with further tuning and data refinement.

Ralph 1.5 Think demonstrates early evidence that reasoning-centric fine-tuning can substantially narrow the gap between small and large language models, reinforcing the viability of SLMs for cost-efficient reasoning tasks.