---
title: 'SmolTalk: Open Dataset for Small LLMs'
url: https://www.emergentmind.com/topics/smoltalk
type: topic
---

# SmolTalk: Open Dataset for Small LLMs

Within the cited literature, **SmolTalk** primarily denotes an open-source post-training and instruction-following dataset created during the development of **SmolLM2**, a 1.7 billion parameter language model. It was introduced because fine-tuning the base model on existing public instruction datasets such as **MagPie-Pro, OpenHermes2.5, UltraChat** did **not yield competitive post-trained model results** compared to other small models, which motivated the creation of a new dataset specifically designed to improve **alignment, conversational ability, and instruction-following performance** in small language models [2502.02737]. In subsequent comparative analysis, SmolTalk was examined side by side with **Tulu-3-SFT-Mix** as one of two prominent open post-training datasets, with particular attention to turn structure, task composition, and sample quality [2506.06522].

## 1. Origin, motivation, and design goal

SmolTalk emerged from a data-centric effort to strengthen post-training for small models. In the SmolLM2 development process, the authors state that existing datasets were at points **problematically small or low-quality**, leading them to introduce new specialized datasets, including **SmolTalk**, for stages where stronger instruction and conversational data were needed [2502.02737]. The dataset is described as a **curated mixture of newly generated synthetic datasets and carefully selected public datasets**, with a **strong emphasis on conversational, reasoning, and instruction-following coverage** [2502.02737].

A later comparative study characterizes SmolTalk as targeting **conversational depth and pragmatic rewriting** for **“smol” (small) models**, with the aim of providing **resource-efficient, rich multi-turn dialog without requiring large compute** [2506.06522]. The same study describes the dataset’s motivation as covering **practical tasks** and enhancing **conversational fluency, context-handling, and non-trivial dialog for small LLMs** [2506.06522]. Taken together, these descriptions place SmolTalk at the intersection of alignment data engineering, multi-turn dialogue modeling, and efficient post-training for sub-2B models.

## 2. Construction and internal composition

SmolTalk has a reported total size of **1.1 million instruction-response pairs** [2502.02737]. Its construction combines newly generated synthetic conversational datasets, task-specific resources, and selected existing instruction data.

| Dataset source | # Samples in SmolTalk |
|---|---:|
| MagPie-Ultra | 431k |
| Smol-Rewrite | 56.2k |
| Smol-Constraints | 36.2k |
| Smol-Summarization | 101k |
| NuminaMath-CoT | 112k |
| MetaMathQA | 50k |
| Self-OSS-Starcoder2-Instruct | 50.7k |
| APIGen-Function-Calling | 87.5k |
| SystemChats2.0 | 35.9k |
| LongAlign | 3.73k |
| Everyday-Conversations | 2.38k |
| Explore-Instruct-Rewriting | 32k |
| OpenHermes2.5 | 100k |

The newly generated components include **MagPie-Ultra**, **Smol-Constraint**, **Smol-Rewrite**, and **Smol-Summarization** [2502.02737]. **MagPie-Ultra** is described as **1 million three-turn conversation samples**, generated using **Llama-3.1-405B-Instruct** with system prompts and then filtered and deduplicated [2502.02737]. **Smol-Constraint** contains **36k+ instructions with strict and detailed constraints**, targeting **hard instruction-following (IFEval-like) behavior** [2502.02737]. **Smol-Rewrite** and **Smol-Summarization** focus on **high-quality text rewriting and summarization**, using synthetic source texts created with **PersonaHub** and **FinePersonas**, then rewritten with **Qwen2.5-72B-Instruct** [2502.02737].

The task-specific and domain-specific portions include **NuminaMath-CoT**, **MetaMathQA**, **Self-OSS-Starcoder2-Instruct**, **APIGen-Function-Calling**, **SystemChats2.0**, **LongAlign**, **Everyday-Conversations**, and **Explore-Instruct-Rewriting** [2502.02737]. The dataset also includes **100k random OpenHermes2.5 samples** for **knowledge, and broad coverage** [2502.02737].

Quality control is described in concrete terms. Synthetic generation used **leading models** with **system prompts engineered for diversity, careful topic balance, and depth**. Filtering was performed using **small Llama models** and **custom reward models (ArmoRM)**, alongside **safety filtering (Llama-Guard-3-8B)**. Deduplication used **advanced embedding-based and semantic similarity (gte-large-en-v1.5)** [2502.02737]. A later study additionally states that all subsets underwent **deduplication, quality filtering, n-gram decontamination (as in Tulu)** [2506.06522].

## 3. Structural profile and annotated quality

Using the **Magpie framework**, a comparative study annotated SmolTalk at the sample level with metrics for **turn structure**, **task category**, **input quality**, and **response quality** [2506.06522]. After annotation, the dataset contained **1,024,791 valid samples** [2506.06522].

The most distinctive structural property is turn organization. **70% of SmolTalk is Multi-turn (MT)**, corresponding to **718,164 MT samples**, with the remaining **306,627** being single-turn [2506.06522]. The dataset is reported as being **dominated by 6-turn convos (39.8% of data), then 3-turn convos (28%)** [2506.06522]. By task category, **brainstorming, role playing, creative writing** are reported as **>90% multi-turn**, whereas **Math ST: 68%** and **Coding ST: 53.6% of respective categories** [2506.06522].

Task category distribution was reported as **General 57.6%**, **Knowledge Recall 9.4%**, **Math 4.6%**, **Reasoning 9.9%**, **Coding 7.1%**, **Safety 3.3%**, and **Precise IF tasks 8.2%** [2506.06522]. The same analysis states that **Conversational/Creative Tasks**—including **brainstorming, editing, advice seeking, role playing, creative writing**—make up **~30%**, which is **much higher than Tulu's ~10%** [2506.06522]. By contrast, **Math & Coding** are **4.6% and 7.1% respectively**, both lower than Tulu’s **21.5% and 15.5%** [2506.06522].

Input quality was summarized as **61.2% “excellent,” 23.8% “good”**, meaning **85%+ are good/excellent** [2506.06522]. The same study reports that SmolTalk has a **lower incidence of “poor”/“very poor” (8.5% combined) than Tulu** [2506.06522]. By turn type, **ST: 95% excellent/good** and **MT: 80% excellent/good** [2506.06522]. For response quality, single-turn data exhibited a **bell-shaped** reward distribution with **peak at +1, range -9 to +7**, while multi-turn data showed that **nearly 90% scored 5/5**, **~8% scored 4/5**, and **~2% score 0–3/5** [2506.06522].

Additional annotation axes further specify the corpus. The dataset is **99.3% English**, with **negligible multilingual content**, and **99.1% samples flagged “safe”** [2506.06522]. The authors also note that **multi-turn often recovers from poor initial queries via clarification and iterative improvement**, which is used to explain why **input quality–Reward correlation** is **weaker on MT** than on ST [2506.06522]. This suggests that SmolTalk’s multi-turn structure is not merely a formatting property; it is bound up with the dataset’s intended conversational dynamics.

## 4. Role in SmolLM2 post-training

In SmolLM2, SmolTalk was used for the **Supervised Fine-Tuning (SFT) stage** for **2 epochs**, with **sequence length increased to 8192** to maintain long-context capabilities [2502.02737]. The reported SFT hyperparameters were **batch size of 128** and **learning rate of \(3.0 \times 10^{-4}\)** [2502.02737]. The resulting SFT model then underwent **Direct Preference Optimization (DPO)** for **2 epochs**, using **UltraFeedback** for preference data; the authors explicitly note that this **did not affect context ability** [2502.02737].

The SFT phase used the **full SmolTalk mixture**, maintaining a **broad domain balance**, with **empirically tuned proportions per dataset** [2502.02737]. For the smaller **135M** and **360M** models, a **filtered** version of SmolTalk was used, **removing most complex and hard instruction samples**, in order to better match model capacity [2502.02737].

Ablation studies compared SmolLM2 fine-tuned on **OpenHermes2.5**, **UltraChat**, **MagPie-Pro**, and SmolTalk-related mixtures [2502.02737]. The authors report that fine-tuning on **only MagPie-Ultra** and then **incrementally adding Smol-constraint/rewrite/summarization** produced **stepwise improvements**, particularly on **instruction-following (IFEval), text rewriting, and math** [2502.02737]. They also report that mixing **MagPie-Ultra+** with either **NuminaMath-CoT** or **MetaMathQA** improved **different math benchmarks**, and therefore **both were included in SmolTalk** [2502.02737].

The benchmark values given for **SmolTalk** were **IFEval 46.67**, **MT-Bench 5.49**, **GSM8K 43.75**, **MATH 18.60**, **ARC 40.02**, and **MMLU-Pro 18.19** [2502.02737]. The final **SmolLM2-SFT†**, trained on the full SmolTalk mixture, reached **IFEval 57.09**, **MT-Bench 6.11**, **GSM8K 47.54**, **MATH 19.64**, **ARC 42.49**, and **MMLU-Pro 19.06** [2502.02737]. The paper states that the **final SFT checkpoint using the full SmolTalk dataset achieved the strongest results across major benchmarks** [2502.02737].

## 5. Comparison with Tulu-3-SFT-Mix and the creation of TuluTalk

The comparative study in [2506.06522] frames SmolTalk and **Tulu-3-SFT-Mix** as complementary open post-training datasets. SmolTalk is summarized as **70% multi-turn**, whereas Tulu is **95% single-turn** [2506.06522]. In qualitative terms, **SmolTalk** is described as **strong for fluency in conversation, dialog adaptation, natural turn-taking**, while **Tulu** is described as **strong for factual accuracy, structured reasoning, and coding/math protocols** [2506.06522].

The quantitative contrast is also explicit. SmolTalk has **4.6% math** and **7.1% coding**, compared with Tulu’s **21.5%** and **15.5%** [2506.06522]. The same study states that **SmolTalk SFT outperforms Tulu on OpenLLM Leaderboards; Tulu does better at code benchmarks. Both > Orca baseline** [2506.06522]. This comparison is central to the design of **TuluTalk**, a curated mixture built from both sources.

The TuluTalk construction pipeline is reported in three steps: **Step 1** filters both datasets for **high input and response quality** using Magpie; **Step 2** identifies **underrepresented task categories** in the filtered result; **Step 3** re-adds samples from underrepresented tasks—**often drawn from SmolTalk**—to restore task diversity [2506.06522]. The resulting dataset, **TuluTalk**, is reported as **808k samples**, **14% smaller than Tulu**, and **27% smaller than SmolTalk**, while **matching or exceeding** the source datasets on benchmarks [2506.06522]. The study attributes a specific role to SmolTalk here: it supplied the **bulk of multi-turn, dialogically rich, and creative instruction samples** in the final blend [2506.06522].

## 6. Extensions, derivatives, and terminological scope

SmolTalk has also served as a template for derivative corpora. **Smoltalk-chinese**, introduced as part of the **OpenCSG Chinese Corpus**, is described as a **high-quality, open-source synthetic dataset of Chinese multi-turn, chat-format conversations** for LLM training and instruction fine-tuning [2501.08197]. It is explicitly said to be **inspired by SmolTalk** and **Magpie-ultra-1M**, while extending them from **11 task categories** to **18 categories** by adding **format constraints, summarization, rewriting, document-QA, safe-QA, translation, everyday-talk** [2501.08197]. Construction used **Deepseek-V2.5** and **Qwen2.5-72B-Instruct**; the first user query in each dialog was scored using **Qwen2.5-7b-instruct**, and only conversations with **score > 3 out of 5** were retained [2501.08197]. Deduplication used **gte-zh-large**, with cosine similarity thresholds of **0.8 for multi-turn dialogs** and **0.7 for single-turn samples** [2501.08197]. The final size is reported as **~70,000 high-quality samples**, and fine-tuning on this dataset achieved the **strongest overall performance** on **AlignBench** among the datasets compared [2501.08197].

The name also appears in adjacent literatures with different meanings. In one human-robot interaction study, **robot-initiated small talk** is termed **“SmolTalk”** and tested in a collaborative assembly task using a **Franka Emika Panda** robotic arm; the study reports **significantly higher levels of rapport** in the social condition, while participants in that condition also recorded **longer task durations** [2501.13233]. In multimodal language modeling, the similarly named **SmolTolk** denotes a family of **Text-Speech Language Models** built on SmolLM; that paper explicitly states that **SmolTolk** is **not synonymous with, or related to, other existing “SmolTalk” models or agents** [2503.06211]. These distinctions matter because the dominant technical usage in language-model post-training refers to the dataset lineage described above, whereas the label is reused in robotics and multimodal speech-text work for different objects of study.

SmolTalk’s significance in small-model research therefore lies in a specific combination of properties documented across these papers: it is **dialog-heavy**, **quality-filtered**, **task-diverse**, and directly tied to the empirical post-training gains of **SmolLM2** and to later curation work such as **TuluTalk** [2502.02737]. A plausible implication is that SmolTalk’s main research value is not only its scale, but the way it operationalizes multi-turn dialogue, pragmatic rewriting, and hard instruction-following within an open post-training corpus for resource-constrained language models.

Source: https://www.emergentmind.com/topics/smoltalk