Atomight V2.5 AdjacentGen
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Atomight-V2.5-1.7B-C1
Atomight V2.5 1.7B, our most capable and intelligent model yet, is a multi-domain reasoning model built on Qwen3-1.7B, trained with GRPO (Group Relative Policy Optimization) across STEM, science, math, and code, using a curated six-dataset stack in a streaming round-robin setup — trained entirely on a single Colab free-tier T4 GPU. Note: This is only the first checkpoint model of Atomight-V2.5-1.7B, but still usable and powerful model.
Overview
Atomight-V2.5 is post-trained from Qwen/Qwen3-1.7B (already instruct/chat-capable) using memory-efficient chunked GRPO to fit T4 VRAM constraints. Training data spans four domains, each selected for having genuinely verifiable rewards (exact-match answers or executable test cases) rather than relying purely on LLM-judged quality:
| Domain | Dataset(s) | Role |
|---|---|---|
| STEM | Logics-MLLM/Logics-STEM-SFT-Dataset-Open-5.3M |
Broad STEM coverage, difficulty-annotated |
| Science | MegaScience/MegaScience |
Science-specific depth, ablation-selected subsets |
| Math | nvidia/OpenMathReasoning, zwhe99/DeepMath-103K |
Verifiable-answer CoT and RL-native reward data |
| Code | nvidia/OpenCodeInstruct |
Instruction-tuned code generation with test cases |
Reward shaping combines an XML-format reward (<thinking> / <answer> tag structure) with an exact-match correctness reward, evaluated via trl's GRPOTrainer on 4-bit QLoRA (Unsloth).
Benchmark Results
Evaluated with EleutherAI's lm-evaluation-harness, standard few-shot settings, no sample limiting.
| Benchmark | Score |
|---|---|
| GSM8K (strict-match) | 69.6% |
| GSM8K (flexible-extract) | 69.9% |
| MMLU (overall) | 55.7% |
| ARC-Challenge (acc_norm) | 43.0% |
| HellaSwag (acc_norm) | 60.4% |
| Winogrande | 61.1% |
| TruthfulQA (mc2) | 45.9% |
| HumanEval (pass@1) | 40.2% |
| MBPP (pass@1) | 42.8% |
Full benchmark profile
MMLU breakdown
| Category | Score |
|---|---|
| STEM | 53.9% |
| Social Sciences | 63.8% |
| Humanities | 48.7% |
| Other | 60.0% |
Progress vs. Atomight-V2.1-0.5B
V2.5 (1.7B) is compared here against the previous-generation V2.1 (0.5B) on the benchmarks both models were evaluated on. Note this comparison reflects both the parameter increase (0.5B → 1.7B) and the change in training methodology — it isn't an isolated ablation of either factor alone.
| Benchmark | V2.1-0.5B | V2.5-1.7B | Δ |
|---|---|---|---|
| GSM8K (strict) | 19.8% | 69.6% | +49.8 |
| GSM8K (flexible) | 32.4% | 69.9% | +37.5 |
| ARC-Challenge | 33.8% | 43.0% | +9.2 |
| HellaSwag | 52.4% | 60.4% | +8.0 |
Notably, the gap between GSM8K's strict-match and flexible-extract scores nearly closed between versions (12.6-point gap in V2.1 → 0.3-point gap in V2.5), indicating the GRPO reward shaping improved not just raw math ability but the model's consistency in producing cleanly parseable, correctly formatted answers.
Known Limitations
Word-problem math vs. abstract math. Despite strong GSM8K performance, Atomight-V2.5 underperforms on MMLU's more abstract/formal math and physics subcategories:
| Category | Score |
|---|---|
| GSM8K (word problems) | 69.6% |
| MMLU College Mathematics | 38.0% |
| MMLU High School Mathematics | 35.9% |
| MMLU College Physics | 34.3% |
| MMLU Abstract Algebra | 39.0% |
This suggests the training data (competition/olympiad-style word problems) taught strong step-by-step arithmetic reasoning, but that skill did not fully transfer to more abstract, formally-structured mathematics.
Truthfulness. TruthfulQA (45.9%) was not a training target — the model has no specific resistance to plausible-but-false completions beyond what emerges from base pretraining.
Code generalization. HumanEval/MBPP scores (40-43%) are solid for model size but sit below the model's math performance in relative terms, suggesting a similar narrow-transfer pattern to the math gap above.
Training Infrastructure
- Base model:
Qwen/Qwen3-1.7B(post-trained, not a base checkpoint) - Method: GRPO via
trl, 4-bit QLoRA via Unsloth (r=16,lora_alpha=32) - Hardware: Single Google Colab free-tier Tesla T4 (16GB)
- Memory strategy: Streamed, chunked micro-batches (20 samples/chunk) pulled round-robin across all six datasets, trained, then discarded — avoids materializing multi-million-row datasets in memory
- Reward functions: XML-format adherence (
<thinking>/<answer>tags) + exact-match correctness against verifiable answers
Evaluation Methodology
All scores use standard lm-evaluation-harness task configs at default few-shot settings, with no --limit sampling — results are intended to be directly comparable to other models' published numbers on the same tasks. Code benchmarks (HumanEval, MBPP) were run with HF_ALLOW_CODE_EVAL=1 in a sandboxed Colab runtime.
Citation
If you use this model, please cite the underlying datasets and base model:
Base model: Qwen/Qwen3-1.7B (Qwen Team, Alibaba)
Training datasets: Logics-STEM-SFT-Dataset-Open-5.3M, MegaScience,
OpenMathReasoning, DeepMath-103K, OpenCodeInstruct
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