Darwin-27B-RSI

Darwin-27B-RSI: A Model That Improved Itself — Learning Only From Its Own Solutions

Qwen3.5-27B family · 27B dense · Thinking mode · BF16 · Apache 2.0 No human-written answers. The model generated its own learning signal — and got measurably better.

Abstract

Darwin-27B-RSI is Darwin-27B-Opus after Recursive Self-Improvement (RSI): the model was improved using only signal it produced itself. During self-improvement, the model itself (its weights) improves by learning only from its own solutions. No human-written solutions or reasoning traces are used; correctness is checked automatically (agreement across its own samples and code execution).

Under an identical evaluation protocol, Darwin-27B-RSI improves over its parent on graduate-level science reasoning — +5.24 points on GPQA Diamond (single sample) and +3.79 points with majority voting — with every gain statistically significant in paired tests.

As the reasoning engine of Darwin-27B-JEV on the Decision Index, it lifts the hardest reasoning decisions: GPQA Diamond skill 0.31 → 0.71, GSM8K 0.61 → 0.97, MMLU-Pro 0.60 → 0.82.

Model-level RSI vs. harness-level RSI

Darwin-27B-RSI is Model-level RSI: the model itself (its weights) improves by learning only from its own solutions. No human-written solutions or reasoning traces are used; correctness is checked automatically. You download a new model file, and it is smarter on its own.

Harness-level RSI (for example, Google's RRSI) improves the prompts, tools and workflow around a fixed model — like rewriting an employee's manual, while Model-level RSI is the employee getting smarter. The two are complementary: a harness-level loop can run on top of a Model-level RSI model.

What Is RSI?

Most models improve only when people write more answers for them. Recursive Self-Improvement removes that bottleneck: the model works on problems, judges its own work, and learns from what it produced — then repeats. Each improved model becomes the starting point for the next improvement.

Darwin-27B-RSI demonstrates this loop on a 27B model:

• Human-written solutions or reasoning traces used: 0
• Direction of change: measurably better on held-out graduate-level science
• Contamination check: training problems share 0 items with the evaluation sets reported here

The training procedure itself is not released.

Results

Science reasoning (same protocol for both models)

Both models were measured under the same protocol (single sample, identical sampling settings and token budget), so numbers differ from the Darwin-27B-Opus card, which reports a different protocol. All gains are statistically significant in paired tests.

Decision Index — as the reasoning engine of Darwin-27B-JEV

The Decision Index scores typed-decision engines on 43 benchmarks and ~121K decisions (chance-corrected: 0 = random, 1 = perfect). Darwin-27B-RSI handles the decisions that need real thinking:

= gold benchmark (weighted 1.2× on the board). Darwin-27B-JEV: ≈ 61.1 under the v0.2.1 board rules (our recomputation; official score pending review). Full run: FINAL-Bench/Darwin-27B-JEV-decision-index.

Usage

Darwin-27B-RSI is a thinking model. Give it room to reason and read the answer after the reasoning block.

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "FINAL-Bench/Darwin-27B-RSI"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "A ball is thrown upward at 40 m/s. For how long is it above 40 m? (g = 10 m/s²) Think, then give the final answer."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=8192, temperature=0.6, top_p=0.95, do_sample=True)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

vLLM

vllm serve FINAL-Bench/Darwin-27B-RSI --max-model-len 32768

Recommended: temperature 0.6, top_p 0.95, generous token budget (8K–16K) for hard problems.

Model Details

Limitations and Disclosure

• Gains were measured on graduate-level science; other domains may change less.
• As a thinking model, it can produce long reasoning; cap max_new_tokens for latency-sensitive use.
• 31 training problems (0.22% of the benchmark) overlap with the Decision Index MMLU set; the model learned only from its own solutions to them.
• Not affiliated with TypeSafe AI or its Jev product.

Citation

@misc{darwin27b_rsi_2026,
  title  = {Darwin-27B-RSI: Recursive Self-Improvement without Human Labels},
  author = {VIDRAFT and FINAL-Bench},
  year   = {2026},
  url    = {https://huggingface.co/FINAL-Bench/Darwin-27B-RSI}
}