DeepSeek-R1 Distilled Qwen 32B LLM

• DeepSeek-R1-Distill-Qwen-32B is a 32-billion-parameter dense language model derived via supervised distillation, capturing chain-of-thought reasoning from an RL-optimized teacher.
• The model is trained using a combination of token-level cross-entropy and Kullback–Leibler divergence losses on millions of CoT examples, achieving robust performance on math, coding, and reasoning benchmarks.
• While excelling in standard tasks, the model shows limitations in long-context and multi-hop reasoning challenges, highlighting opportunities for further methodological improvements.

DeepSeek-R1-Distill-Qwen-32B is a 32-billion-parameter dense LLM produced via supervised distillation from advanced reasoning-focused teacher models in the DeepSeek-R1 series. It utilizes the Qwen2.5-32B decoder-only transformer backbone and is trained to replicate the stepwise chain-of-thought (CoT) reasoning behaviors exhibited by RL-optimized teacher models such as DeepSeek-R1. This model has been widely benchmarked in mathematical, coding, and multi-domain reasoning tasks, subjected to detailed mechanistic analysis, and has served as a baseline for further tool use and post-training interventions.

1. Model Architecture and Distillation Process

DeepSeek-R1-Distill-Qwen-32B inherits its architecture from the Qwen2.5-32B base: 64 transformer blocks, model dimension 8 192, feedforward dimension 32 768, with 64 attention heads per layer, totaling approximately 32 billion parameters. No adapters, LoRA modules, or architecture modifications are introduced during distillation (Zhuang et al., 25 Feb 2025, DeepSeek-AI et al., 22 Jan 2025, Zhang et al., 28 Sep 2025).

The distillation process is classic teacher–student knowledge distillation, where the teacher (DeepSeek-R1, itself a 37B-activated-parameter MoE model with RLHF or direct RL optimization) generates full step-by-step CoT traces and final answers on diverse tasks (e.g., math, code, multi-step reasoning). These outputs serve as "soft targets" for the student. The standard loss comprises a mixture of token-level cross-entropy loss and Kullback–Leibler divergence between teacher and student output distributions: $$L_{\mathrm{total}} = \lambda\,L_{\mathrm{KD}} + (1-\lambda)\,L_{\mathrm{CE}}$$ with $$L_{\mathrm{KD}}(x) = \mathrm{KL}(p_T(\cdot|x)\,\|\,p_S(\cdot|x))$$ and $L_{\mathrm{CE}} = - \log p_S(y|x)$. The exact value of $\lambda$ is not disclosed (Zhuang et al., 25 Feb 2025). Throughout, the student is forced, token-by-token, to imitate both the teacher's chain-of-thought and the final answer (DeepSeek-AI et al., 22 Jan 2025, Zhang et al., 28 Sep 2025).

In most published pipelines, the distillation data set consists of hundreds of thousands to over one million examples, including both synthetic teacher traces on mathematical/coding problems and instruction-tuning corpora (reasoning, factual QA, general writing) (DeepSeek-AI et al., 22 Jan 2025, Zhao et al., 25 Mar 2025). Additional verification is sometimes performed, such as filtering for answer agreement, code correctness (via test cases), and format or length consistency (Zhao et al., 25 Mar 2025). No curriculum, explicit reward shaping, or RL is used during distillation to Qwen-32B (DeepSeek-AI et al., 22 Jan 2025, Zhuang et al., 25 Feb 2025).

2. Reasoning Capabilities and Benchmarks

DeepSeek-R1-Distill-Qwen-32B is designed as a reasoning-enhanced model, aiming to transfer the RL-induced slow-thinking CoT strategies of DeepSeek-R1 into an efficient dense model. On standardized benchmarks, the distilled model produces explicit reasoning traces (> …</think>) and final answers, mimicking the teacher style (Zhang et al., 28 Sep 2025).

On math, code, and GPQA science tasks, it achieves high pass@1 rates: 72.6% (AIME 2024), 94.3% (MATH-500), 62.1% (GPQA-Diamond), and 57.2% (LiveCodeBench), with an overall average of 71.6% (Zhao et al., 25 Mar 2025, DeepSeek-AI et al., 22 Jan 2025). On the A-Eval-2.0 multi-domain benchmark, it scores 75–82/100 in text understanding, extraction, and generation, 78/100 in logical reasoning, and 88/100 in task planning (Zhao et al., 16 Feb 2025).

However, its performance on long-context, process-heavy reasoning tasks is suboptimal. For example, on the DocPuzzle 100-item multi-step benchmark, it lags substantially behind its teacher (39.7% versus 66.3%) and behind top "instruct" LLMs with zero-shot CoT prompting (e.g., Claude 3.5 Sonnet at 57.7%) (Zhuang et al., 25 Feb 2025). The gap widens on problems requiring deep multi-hop inference and robust generalization, suggesting limits of direct SFT-style distillation for reasoning transfer.

Model Family	AIME-2024	MATH-500	GPQA-Diamond	LiveCodeBench	DocPuzzle	A-Eval Logical Reasoning
DeepSeek-R1	~79.8	~98.2	~71.5	~65.9	66.3	>85
Qwen2.5-32B-Instruct	~51.2	~85.0	~52.3	~40.1	45.0	~76
DeepSeek-R1-Distill-Qwen-32B	72.6	94.3	62.1	57.2	39.7	78

3. Analysis of Mechanistic Reasoning and Model Structure

Recent mechanistic investigations into the distilled DeepSeek R1 models reveal that explicit CoT token sequences actively influence answer generation (Zhang et al., 28 Sep 2025). Empirical ablation demonstrates that including <think>… traces in inference improves scores by 5–10 points on MATH-500 and by 4–7 points on multi-domain tasks, with effect sizes diminishing but persisting at higher model scales.

Attention analyses show mid-layer Reasoning-Focus Heads (RFHs), typically layers 14–22 (in smaller versions), whose answer tokens dynamically attend to preceding reasoning tokens, tracking the evolution of the reasoning chain. Heat-maps reveal clear diagonals, indicating an information flow from each reasoning step into corresponding answer tokens, with spikes for self-reflective cues (e.g., "wait", "note") (Zhang et al., 28 Sep 2025).

Mechanistic interventions using activation patching further demonstrate functional causality: corrupting or restoring activations at specific reasoning tokens can change the answer tokens' logits by 0.4–0.7 units (normalized logit difference), confirming a directional role of intermediate computations. These findings substantiate that reasoning tokens are not spurious artifacts but play a functional role during autoregressive decoding.

4. Failure Modes, Limitations, and Comparative Analysis

Though DeepSeek-R1-Distill-Qwen-32B attains state-of-the-art results on well-aligned math/code datasets, its generalization to unseen or process-heavy settings is limited (Zhuang et al., 25 Feb 2025, Zhao et al., 25 Mar 2025). On DocPuzzle and similar long-context benchmarks, its accuracy can fall more than 25 points below its teacher, consistent with surface-level imitation and weak process abstraction. The main failure modes identified are:

• Surface memorization: The model often regurgitates the shape and structure of reasoning seen during SFT, without robust algorithmic generalization.
• Low exploration: Under deterministic or low-temperature generation, it produces highly repetitive solutions, with negligible gains from sampling multiple completions (pass@3).
• Poor out-of-distribution generalization: Performance collapses on multi-hop or long-context tasks outside its SFT curriculum (Zhuang et al., 25 Feb 2025).
• Sensitivity to quantization: No dedicated quantized variant is published, but compression to 4-bit is expected to degrade logical reasoning faster than text generation (Zhao et al., 16 Feb 2025).

Comparisons to alternative distillation/mixing approaches demonstrate that both curriculum training with difficulty filtering (Wen et al., 13 Mar 2025) and Branch-Merge domain fusion (Sun et al., 6 Mar 2025) can further elevate accuracy and generalization while reducing compute cost or training data redundancy.

5. Enhanced Distillation, Dataset Curation, and Emerging Methodologies

Construction of large, open-source, high-quality distillation corpora—such as the AM-DeepSeek-R1-Distilled-1.4M dataset—improves coverage and verification, enabling the training of next-generation Qwen-32B models with modest but consistent gains over the original DeepSeek-R1-Distill-Qwen-32B on math, code, and cross-domain reasoning (Zhao et al., 25 Mar 2025). Rigorous de-duplication, filtering for answer/test-case correctness, and leveraging in-domain reward modeling reduce noise and improve the reliability of reasoning traces.

Recent advances in post-distillation training highlight further gains:

• Tool-augmented reasoning: By applying Code-Optimized Reasoning Training (CoRT), with hint-injection and rejection fine-tuning, the model's efficiency in code-augmented math improves by 4% absolute on average, with a 30–50% reduction in token usage and latency compared to natural-language-only CoT (Li et al., 23 Oct 2025).
• Curriculum SFT and preference-based fine-tuning: Approaches such as Light-R1 adapt a two-stage difficulty-based SFT filter and Direct Preference Optimization (DPO), yielding 4–10 point increases in competitive math reasoning benchmarks (Wen et al., 13 Mar 2025).
• Domain specialization and parameter fusion: The Branch–Merge distillation pipeline (specialists + Arcee Fusion) achieves up to 5.5 points improvement in math on the same Qwen-32B backbone and narrows the gap to DeepSeek-R1 teacher performance at 1/20th the computational cost (Sun et al., 6 Mar 2025).

6. Applications and Best Practices

DeepSeek-R1-Distill-Qwen-32B delivers high accuracy and robust reasoning on established math/code/science benchmarks, making it suitable for cost-sensitive deployments, especially where dense model latency and VRAM constraints make larger MoE or 70B+ models impractical (Zhao et al., 16 Feb 2025). For general virtual assistant tasks requiring strong text generation and planning, it occupies an optimal cost–performance frontier, achieving A–A+ tier performance in A-Eval task planning with moderate VRAM requirements.

Domain-specific evaluations (e.g., retrieval-augmented generation in finance/biomed/cybersecurity) reveal superior alignment and span identification compared to non-distilled and alternative models. The model achieves high intersection-over-union in token-level retrieval and exhibits consistent segment-level reasoning behaviors, though chunk sizing and domain adaptation must be tuned individually (Jadon et al., 21 Feb 2025).

For specialized automation, such as CFD file synthesis and multi-step engineering workflows, initial evaluations suggest that, without domain-specific fine-tuning, DeepSeek-R1-Distill-Qwen-32B underperforms stronger or larger teacher models regarding solution stability, but can be integrated into cost-effective, human-in-the-loop pipelines for routine cases (Wang et al., 2 Apr 2025).

Best practices in deployment include retaining explicit reasoning+answer output formatting, monitoring reasoning token alignment for safety or debugging, providing tool augmentation when efficiency is essential, and pursuing further domain-adaptive fine-tuning to bridge remaining capability gaps.

7. Future Directions and Open Challenges

Substantial accuracy and generalization gaps relative to RL-optimized teachers on process-heavy, out-of-domain, or long-context reasoning benchmarks have motivated several research priorities:

• Reinforcement learning on the student: Direct RLHF or policy optimization on distilled Qwen-32B could potentially recover multi-step, generalizable reasoning (Zhuang et al., 25 Feb 2025, Wen et al., 13 Mar 2025).
• Chain-of-thought ranking and contrastive losses: Supplementing SFT with objectives that rank or penalize suboptimal reasoning traces may prevent surface imitation.
• Data diversification and self-improvement: Expanding distillation curricula beyond math/code to realistic document-based puzzles—potentially with iterative chain refinement or self-critique—may enhance abstraction (Zhuang et al., 25 Feb 2025).
• Mechanistic interpretability: Leveraging attention-head or activation patching analyses to diagnose failure points and inform surgical interventions (Zhang et al., 28 Sep 2025).
• Hybrid tool-use frameworks: Integrating code interpreters or external modules with minimal data and tightly controlled chain hand-offs can improve both faithfulness and efficiency (Li et al., 23 Oct 2025).

Broader availability of open datasets and checkpoint releases facilitates further research in scalable reasoning transfer, process-aware evaluation, and safe deployment of distilled LLMs in both academic and industrial environments.

---

Key references:

(Zhuang et al., 25 Feb 2025, Zhao et al., 25 Mar 2025, Jadon et al., 21 Feb 2025, Zhao et al., 16 Feb 2025, DeepSeek-AI et al., 22 Jan 2025, Sun et al., 6 Mar 2025, Wen et al., 13 Mar 2025, Zhang et al., 28 Sep 2025, Li et al., 23 Oct 2025, Wang et al., 2 Apr 2025)