DeepScaleR: A Small Reasoning Model Tested on Competition Math

In February 2025, Matthew Berman reviews DeepScaleR-1.5B-Preview, a Berkeley research model fine-tuned from DeepSeek-R1-Distill-Qwen-1.5B using reinforcement learning. He is excited by a small model exceeding OpenAI’s o1-preview on selected competition-math benchmarks. That result does not establish superiority on all mathematics, general knowledge or other tasks.

The researchers describe distributed reinforcement learning and longer-context training. Berman guesses that distributed might mean clusters spread around the world, but the term alone does not establish their physical locations. The team’s report says its work used 40,000 math problems and progressively scaled response lengths.

The team reports 43.1% pass@1 on AIME 2024 for DeepScaleR versus 40.0% for o1-preview, and 87.8% versus 81.4% on MATH-500 in its table. These are benchmark-specific historical results with particular evaluation settings, not proof that a 1.5B model exceeds o1-preview on every math problem or all versions of o1.

Berman contrasts an outcome reward, which assesses the final answer, with a process reward that gives feedback on intermediate steps. He prefers process feedback because it can identify where reasoning went wrong. That is his view; the video does not show an experiment establishing that process rewards are always better.

The research team argues that reinforcement learning can improve a small model already initialized with high-quality distilled reasoning data. Its reported AIME 2024 score rose from 28.8% for the starting model to 43.1% for DeepScaleR. Berman sees this as evidence that specialized small models may become useful for narrow tasks; it does not imply that the same improvement transfers to all domains.

The researchers report approximately 3,800 A100 GPU-hours and $4,500 for their training run. This is the reported cost of their run, not the full cost of creating the base model, original distillation, data, research and infrastructure. Berman notes that the team released model weights and training code, enabling others to inspect and build on the work.

Berman downloads a quantized GGUF version and runs it locally in LM Studio on an M2 Mac while recording and performing another training task. He estimates the displayed quantized file at about 1.12 GB, whereas the full-precision file is larger. Hardware memory, context length and runtime affect whether a particular device can run it; a phone demonstration is not shown.

He gives the model an AIME problem. It produces a lengthy response and a final answer that Berman suspects is wrong; the screen shows approximately 21,618 generated tokens at 44.7 tokens per second in this run. A long explanation or fast generation does not establish correctness, and one quantized local test cannot reproduce the full benchmark.

Berman concludes that small, locally run models can be useful for specialized reasoning. He is an investor in LM Studio, as disclosed in the video description. The broader comparison remains limited to the researchers’ reported tests and the single local demonstration.