DeepSeek V3.1 Reasoner: Non-Reasoning Baseline

• The paper introduces DeepSeek V3.1 Reasoner as a 685B-parameter, decoder-only Transformer serving as the non-reasoning baseline for Arabic NLP evaluations.
• It employs a pure autoregressive pretraining regime with zero- and few-shot prompting to highlight performance differences when explicit reasoning heads are absent.
• Empirical results demonstrate that while scale boosts basic performance, integrating explicit reasoning modules can yield significant F1 gains in classification and generative tasks.

DeepSeek V3.1 Reasoner denotes a large-scale, open-source Arabic LLM ("V3-685B") positioned as the primary non-reasoning baseline in comparative evaluations of reasoning-centric LLMs. Developed within the DeepSeek model family, V3.1 is distinguished by its pure pretraining regime, standardized architecture, and absence of explicit architectural designs for incentivized reasoning. Extensive empirical assessment in the AraReasoner benchmark suite positions DeepSeek V3.1 as a reference standard for quantifying the effect of explicit reasoning adaptations in related DeepSeek-R1 and commercial models (Hasanaath et al., 10 Jun 2025).

1. Model Architecture and Training Paradigm

V3-685B is instantiated as a 685-billion-parameter, decoder-only Transformer. Its training procedure adheres strictly to autoregressive next-token prediction over large-scale datasets, with no additional supervision, adapters, or custom attention heads for reasoning traces. The model does not implement the “policy-gradient incentive” mechanism or specialized chain-of-thought (CoT) heads characteristic of the DeepSeek-R1 series. Consequently, DeepSeek V3.1 serves as a control for evaluating performance gains attributable to explicit reasoning modules rather than scale or pretraining data alone (Hasanaath et al., 10 Jun 2025).

No exact architectural schematic or layer-by-layer parameterization is provided in the benchmark; detailed internals are deferred to the cited DeepSeek-V3 technical report (Liu et al. 2024). During evaluation, V3-685B is utilized “out of the box” without any inference-time adapters.

2. In-Context Learning and Prompt Engineering

AraReasoner employs V3-685B exclusively in zero-shot and few-shot (3- and 5-shot) prompting configurations, with prompt templates informed by entropy-based sampling and human curation:

• Sample Selection: AraBERT models score token-level entropy per candidate example. The top-20 highest uncertainty samples are manually reviewed, with five coherent, high-uncertainty examples retained. The leading three form the 3-shot context; all five are used for 5-shot.
• Prompt Structure: Each prompt comprises a system prompt, a single optimal instruction (per task, selected on development data), the selected in-context examples, the query, and the expected response.
• Prompt Variants: For classification tasks, Arabic “instructive” prompts (P4) provide highest F1, while English “instructive” prompts (P1) marginally outperform alternatives on generative tasks; “role-playing” prompt variants underperform.

The explicit entropy-based method for assembling in-context examples demonstrably boosts classification and reasoning performance, indicating a pronounced sensitivity to example selection over raw context length (Hasanaath et al., 10 Jun 2025).

3. Fine-Tuning Methodology: LoRA Procedures

While DeepSeek V3.1 is evaluated solely with prompt-based inference, AraReasoner details parameter-efficient fine-tuning only for the R1 models, using Low-Rank Adaptation (LoRA).

• LoRA Update Rule:

$W' = W_0 + \Delta W;\quad \Delta W = A \cdot B$

with $A \in \mathbb{R}^{d \times r}$, $B \in \mathbb{R}^{r \times k}$, $r \ll \min(d, k)$.

• Layer Coverage: All $q_{\text{proj}}$, $k_{\text{proj}}$, $v_{\text{proj}}$, $o_{\text{proj}}$, $gate_{\text{proj}}$, $up_{\text{proj}}$, $down_{\text{proj}}$ in every Transformer block (per Hayou et al. 2024).
• Hyperparameters: Optimizer is 8-bit AdamW, learning rate $2 \times 10^{-4}$, batch size 4 (via gradient accumulation), and sequence length $2048$ tokens, with mixed precision (FP16/BF16).

This suggests LoRA-based fine-tuning, particularly with broad applicability to both attention and MLP sublayers (“LoRA-plus”), is critical to bridging performance on morphosyntactic inference tasks, but such adaptation is not part of V3.1’s benchmarked protocol (Hasanaath et al., 10 Jun 2025).

4. Comparative Benchmarks and Empirical Findings

DeepSeek V3.1’s empirical performance establishes baseline expectations for massive, generic pretraining in Arabic NLP, particularly for morphologically rich and dialectally varied datasets.

Zero-Shot and Few-Shot Results

• Sentence Classification: V3-685B achieves an average zero-shot F1 around 80 (across sentiment analysis, dialect detection, hate speech detection), with +4–6 F1 gain under 3-shot. A fourth or fifth example yields negligible or negative return, with three in-context examples representing the “sweet spot” (+13 F1 over zero-shot).
• Ranking: DeepSeek-R1-671B (reasoning-adapted, 671B params) exceeds V3-685B by 9–12 F1 in zero-shot classification. GPT o4-mini occupies an intermediate position, yielding ≈75 F1.

Generative Tasks

• Generative QA (GQA): F1 scores are closely grouped (R1-671B: 19.0; V3-685B: 19.9; GPT-4o: 20.4).
• BLEU-centric Tasks: V3-685B sits between DeepSeek-R1 and GPT-4o (e.g., on TRL: R1=16.99, V3=20.94, GPT-4o=25.58).

Model Size versus Reasoning Design

Parameter count alone does not guarantee performance on reasoning-centric tasks: V3-685B’s 685B parameters underperform R1-Q32B (32B, reasoning-adapted) by ≈10 F1 on sentence classification, illustrating the impact of explicit reasoning modules independent of scale.

Task	V3-685B (ZS)	R1-671B (ZS)	GPT-o4-mini (ZS)
SC (Avg F1)	~80	~89	~75
GQA (F1)	19.9	19.0	20.4
TRL (BLEU)	20.94	16.99	25.58

5. Key Insights and Ablative Analyses

• In-Context Regime: The largest F1 gains for classification and reasoning are concentrated in the initial three examples; exceeding this context length can degrade performance.
• Reasoning versus Pretraining: Absence of explicit reasoning heads or policy-gradient incentives in V3-685B results in consistent underperformance (by 9–15 F1 depending on task) relative to the R1 series.
• LoRA Adaptive Efficiency: Fine-tuning with LoRA, when applied comprehensively (including MLP sublayers), substantially narrows the gap on tasks requiring morphological reasoning, e.g., PoS and WSD, though V3-685B remains unadapted in mainline experiments.

6. Context, Impact, and Future Directions

DeepSeek V3.1 demonstrates the performance floor for very large, nonspecialized Transformer models in Arabic NLP, particularly highlighting the boundary between scale and explicit incentivization strategies for reasoning. Its role as a non-reasoning baseline is instructive for quantifying the marginal benefit of fine-grained reasoning adaptations such as those in DeepSeek-R1. Empirical findings from AraReasoner indicate that ongoing model scale increases must be paired with task-specific incentivization or architectural augmentation to maximize gains on inference-intensive tasks.

A plausible implication is that future competitive Arabic LLMs should unify the scale of the V3 architecture with the reasoning heads and adapters of the R1 lineage to optimize both generalization and deep linguistic inference (Hasanaath et al., 10 Jun 2025).

7. Summary

DeepSeek V3.1, as represented by the V3-685B model, functions as the state-of-the-art non-reasoning open-source baseline in Arabic LLM evaluation. It is configured as a decoder-only Transformer, evaluated under standardized prompting, and neither incorporates nor benefits from reasoning-incentivized module designs in its core architecture. Its empirical results, obtained without LoRA-based or adapter-based fine-tuning, consistently trail those of reasoning-adapted models by substantial margins in classification, generation, and morphological analysis tasks. The evidence underscores the necessity of architectural and training paradigm innovations—beyond parameter count—to attain superior reasoning competencies in large-scale LLMs for morphologically complex and multilingual domains (Hasanaath et al., 10 Jun 2025).