Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning
Paper ā¢ 2605.14386 ā¢ Published ā¢ 42 ā¢
2
VIDRAFT_LAB
SeaWolf-AI
AI & ML interests
None yet
Recent Activity
new activity about 17 hours ago
FINAL-Bench/Darwin-36B-Opus:Added vision: Qwen3.6-35B-A3B-DarleyQuinn commentedon a paper about 18 hours ago
Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning new activity about 18 hours ago
FINAL-Bench/Darwin-36B-Opus:Fantastic ModelOrganizations
Added vision: Qwen3.6-35B-A3B-DarleyQuinn
š 1
18
#4 opened 2 days ago
by 
nightmedia
commented a
paper about 18 hours ago
Fantastic Model
š„ 1
4
#5 opened about 18 hours ago
by 
mgalyan
APEX Quant Request + Real World Performance
5
#3 opened 17 days ago
by 
el4
reacted to their post with ā¤ļø about 18 hours ago
Post
1823
š§¬ Darwin Family: Zero Gradient Steps, GPQA Diamond 88.89%
How far can we push LLM reasoning *without* training?
Our team at VIDRAFT submitted this paper to Daily Papers yesterday, and it's
currently #3. Huge thanks to everyone who upvoted ā sharing the core ideas below.
š Paper: Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning (2605.14386)
š arXiv: https://arxiv.org/abs/2605.14386
š Model: FINAL-Bench/Darwin-28B-Opus
---
TL;DR
Darwin Family is a training-free evolutionary merging framework.
By recombining the weight spaces of existing LLM checkpoints ā with zero
gradient-based training ā it reaches frontier-level reasoning.
- š Darwin-28B-Opus: GPQA Diamond 88.89%
- šø Zero gradient steps ā not a single B200 or H200 hour needed
- š§¬ Consistent gains across 4B ā 35B scale
- š Cross-architecture breeding between Transformer and Mamba families
- š Stable recursive multi-generation evolution
#Three Core Mechanisms
ā 14-dim Adaptive Merge Genome ā fine-grained recombination at both
component level (Attention / FFN / MLP / LayerNorm / Embedding) and block
level, expanding the prior evolutionary-merge search space.
ā” MRI-Trust Fusion ā we diagnose each layer's reasoning contribution
via an **MRI (Model Reasoning Importance)** signal and fuse it with
evolutionary search through a **learnable trust parameter**. Trust the
diagnostic too much and search collapses; ignore it and search becomes
inefficient ā Darwin learns the balance from data.
ā¢ Architecture Mapper ā weight-space breeding across heterogeneous
families. Attention Ć SSM crossover actually works.
Why It Matters
> Diagnose latent capabilities already encoded in open checkpoints,
> and recombine them ā no gradients required.
Replies and critiques welcome š
How far can we push LLM reasoning *without* training?
Our team at VIDRAFT submitted this paper to Daily Papers yesterday, and it's
currently #3. Huge thanks to everyone who upvoted ā sharing the core ideas below.
š Paper: Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning (2605.14386)
š arXiv: https://arxiv.org/abs/2605.14386
š Model: FINAL-Bench/Darwin-28B-Opus
---
TL;DR
Darwin Family is a training-free evolutionary merging framework.
By recombining the weight spaces of existing LLM checkpoints ā with zero
gradient-based training ā it reaches frontier-level reasoning.
- š Darwin-28B-Opus: GPQA Diamond 88.89%
- šø Zero gradient steps ā not a single B200 or H200 hour needed
- š§¬ Consistent gains across 4B ā 35B scale
- š Cross-architecture breeding between Transformer and Mamba families
- š Stable recursive multi-generation evolution
#Three Core Mechanisms
ā 14-dim Adaptive Merge Genome ā fine-grained recombination at both
component level (Attention / FFN / MLP / LayerNorm / Embedding) and block
level, expanding the prior evolutionary-merge search space.
ā” MRI-Trust Fusion ā we diagnose each layer's reasoning contribution
via an **MRI (Model Reasoning Importance)** signal and fuse it with
evolutionary search through a **learnable trust parameter**. Trust the
diagnostic too much and search collapses; ignore it and search becomes
inefficient ā Darwin learns the balance from data.
ā¢ Architecture Mapper ā weight-space breeding across heterogeneous
families. Attention Ć SSM crossover actually works.
Why It Matters
> Diagnose latent capabilities already encoded in open checkpoints,
> and recombine them ā no gradients required.
Replies and critiques welcome š
- 1 reply
published an article about 19 hours ago
Article
Training-Free Reasoning at 88.89% on GPQA Diamond: How Darwin Family Hit Frontier Scores Without a Single Gradient Step
FINAL-Bench
ā¢ ā¢ 11 posted an update about 19 hours ago
Post
1823
š§¬ Darwin Family: Zero Gradient Steps, GPQA Diamond 88.89%
How far can we push LLM reasoning *without* training?
Our team at VIDRAFT submitted this paper to Daily Papers yesterday, and it's
currently #3. Huge thanks to everyone who upvoted ā sharing the core ideas below.
š Paper: Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning (2605.14386)
š arXiv: https://arxiv.org/abs/2605.14386
š Model: FINAL-Bench/Darwin-28B-Opus
---
TL;DR
Darwin Family is a training-free evolutionary merging framework.
By recombining the weight spaces of existing LLM checkpoints ā with zero
gradient-based training ā it reaches frontier-level reasoning.
- š Darwin-28B-Opus: GPQA Diamond 88.89%
- šø Zero gradient steps ā not a single B200 or H200 hour needed
- š§¬ Consistent gains across 4B ā 35B scale
- š Cross-architecture breeding between Transformer and Mamba families
- š Stable recursive multi-generation evolution
#Three Core Mechanisms
ā 14-dim Adaptive Merge Genome ā fine-grained recombination at both
component level (Attention / FFN / MLP / LayerNorm / Embedding) and block
level, expanding the prior evolutionary-merge search space.
ā” MRI-Trust Fusion ā we diagnose each layer's reasoning contribution
via an **MRI (Model Reasoning Importance)** signal and fuse it with
evolutionary search through a **learnable trust parameter**. Trust the
diagnostic too much and search collapses; ignore it and search becomes
inefficient ā Darwin learns the balance from data.
ā¢ Architecture Mapper ā weight-space breeding across heterogeneous
families. Attention Ć SSM crossover actually works.
Why It Matters
> Diagnose latent capabilities already encoded in open checkpoints,
> and recombine them ā no gradients required.
Replies and critiques welcome š
How far can we push LLM reasoning *without* training?
Our team at VIDRAFT submitted this paper to Daily Papers yesterday, and it's
currently #3. Huge thanks to everyone who upvoted ā sharing the core ideas below.
š Paper: Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning (2605.14386)
š arXiv: https://arxiv.org/abs/2605.14386
š Model: FINAL-Bench/Darwin-28B-Opus
---
TL;DR
Darwin Family is a training-free evolutionary merging framework.
By recombining the weight spaces of existing LLM checkpoints ā with zero
gradient-based training ā it reaches frontier-level reasoning.
- š Darwin-28B-Opus: GPQA Diamond 88.89%
- šø Zero gradient steps ā not a single B200 or H200 hour needed
- š§¬ Consistent gains across 4B ā 35B scale
- š Cross-architecture breeding between Transformer and Mamba families
- š Stable recursive multi-generation evolution
#Three Core Mechanisms
ā 14-dim Adaptive Merge Genome ā fine-grained recombination at both
component level (Attention / FFN / MLP / LayerNorm / Embedding) and block
level, expanding the prior evolutionary-merge search space.
ā” MRI-Trust Fusion ā we diagnose each layer's reasoning contribution
via an **MRI (Model Reasoning Importance)** signal and fuse it with
evolutionary search through a **learnable trust parameter**. Trust the
diagnostic too much and search collapses; ignore it and search becomes
inefficient ā Darwin learns the balance from data.
ā¢ Architecture Mapper ā weight-space breeding across heterogeneous
families. Attention Ć SSM crossover actually works.
Why It Matters
> Diagnose latent capabilities already encoded in open checkpoints,
> and recombine them ā no gradients required.
Replies and critiques welcome š
- 1 reply
updated a
model about 19 hours ago
updated a
Space about 19 hours ago
updated 7
models about 19 hours ago
FINAL-Bench/Darwin-28B-KR
Text Generation ā¢ 27B ā¢ Updated ā¢ 401 ā¢ 10
FINAL-Bench/Darwin-2B-Opus-LoRA
Updated ā¢ 23 ā¢ 1
FINAL-Bench/Darwin-36B-Opus
Text Generation ā¢ 35B ā¢ Updated ā¢ 2.72k ā¢ 59
FINAL-Bench/Darwin-9B-MFP4
Text Generation ā¢ 7B ā¢ Updated ā¢ 525 ā¢ 13
FINAL-Bench/Darwin-28B-Opus
Text Generation ā¢ 28B ā¢ Updated ā¢ 866 ā¢ 26
FINAL-Bench/Darwin-9B-NEG
Text Generation ā¢ 10B ā¢ Updated ā¢ 1.34k ā¢ 40
FINAL-Bench/Darwin-2B-Opus
Text Generation ā¢ 2B ā¢ Updated ā¢ 1.47k ā¢ 16
updated 3
Spaces about 20 hours ago
updated a
dataset about 20 hours ago