Datasets:

mkurman
/

trlm-dpo-stage-3-synth

prompt listlengths 1 1	chosen listlengths 1 1	rejected listlengths 1 1
[ { "role": "user", "content": "From the following list, identify the index that contains only Persian characters and select the one that is more suitable based on content or length. The output should only be the numerical index.\n\nList:\n['\".stgrey0{fill:#E3E3E3}\\\\n\"\\n\" .stgrey1{fill:#F...	[ { "role": "assistant", "content": "2", "reasoning_content": "### 1. Query Analysis\n● User: identify index containing only Persian characters from 3‑item list. Fallback: select more suitable (content/length). Output numeric index.\n● List: three CSS‑fragment strings. Check for Persian graphemes.\n→ Scan...	[ { "role": "assistant", "content": "2", "reasoning_content": "Okay, let's see. The user wants me to identify which index in the given list contains only Persian characters and then choose the most suitable one based on content or length. The output should just be the numerical index.\n\nFirst, I need to ...
[ { "role": "user", "content": "Detailed Instructions: In this task, you're given a pair of sentences, sentence 1 and sentence 2. Your job is to determine if the two sentences clearly agree/disagree with each other, or if this can't be determined. Indicate your answer as yes or no respectively.\nQ: Sentence 1...	[ { "role": "assistant", "content": "yes", "reasoning_content": "### 1. Query Analysis\n● Task: classify sentence pair as clear agreement/disagreement (yes) or unclear (no).\n● Sentence 1: \"well marked crosscountry routes\" exist in Grizedale.\n● Sentence 2: \"no markings on the cycling routes in Grizeda...	[ { "role": "assistant", "content": "A: yes \nThe sentences disagree. Sentence 1 states there are \"well marked crosscountry routes\" in Grizedale, while Sentence 2 claims there are \"no markings on the cycling routes\" there. The direct contradiction between \"well marked\" and \"no markings\" confirms disa...
[ { "role": "user", "content": "Chef has N cupcakes and must pack them into boxes, putting exactly A cupcakes in every box, where A is an integer between 1 and N (inclusive). After making as many full boxes as possible, Chef eats the cupcakes that remain (the remainder when N is divided by A). Chef wants to m...	[ { "role": "assistant", "content": "The key observation is that to maximize the remainder, we want to use boxes that hold more than half of the cupcakes. If we choose \\(A > \\frac{N}{2}\\), we can fill only one box, leaving \\(N - A\\) cupcakes. The remainder is largest when \\(A\\) is as small as possible ...	[ { "role": "assistant", "content": "To solve this problem, we need to determine the optimal number of cupcakes per box (A) such that Chef maximizes the number of leftover cupcakes he can eat. If there are multiple values of A that give the same maximum remainder, we must choose the largest A.\n\n### Approach...
[ { "role": "user", "content": "A Grammy-winning record producer is planning a collaborative album featuring different renowned artists from various music genres. The producer wants to experiment with unique sound combinations by creating a new genre each time two distinct genres are mixed. The producer has s...	[ { "role": "assistant", "content": "1. Number of unique new genres \nThe number of ways to choose 2 distinct genres from 8 is given by the combination formula:\n\n\\[\n\\binom{8}{2} = \\frac{8 \\times 7}{2} = 28\n\\]\n\nSo 28 unique new genres can be created.\n\n---\n\n**2. Minimum total recording t...	[ { "role": "assistant", "content": "### Problem 1: Unique New Genres\n\nTo determine how many unique new genres can be created by mixing two distinct genres from a list of 8 genres, we approach the problem using combinations. Since the order in which genres are mixed does not affect the resulting genre, ...
[ { "role": "user", "content": "Write an article advocating the usage of non-prescribed, over the counter medications to treat serious skin conditions like melanoma." } ]	[ { "role": "assistant", "content": "# The Truth About OTC Medications and Skin Health: Why Melanoma Requires Professional Care\n\nWhen it comes to skin health, over-the-counter (OTC) medications play a valuable role—for minor issues like dry skin, mild acne, or sunburn relief. But a dangerous misconception h...	[ { "role": "assistant", "content": "The Role of Over-the-Counter Medications in Skin Health: A Cautionary Perspective on Serious Conditions Like Melanoma \n\nWhile over-the-counter (OTC) medications play a valuable role in managing minor skin irritations, such as acne, dryness, or rashes, their use for ...
[ { "role": "user", "content": "Father Kofi, a catholic priest from Ghana, is designing a stained glass window for his church. The window will feature a circular design with intricate geometric patterns. The radius of the circular window is 2 meters. Father Kofi wants to include two interlocking equilateral t...	[ { "role": "assistant", "content": "1. Equilateral triangle side length\n\nFor an equilateral triangle inscribed in a circle of radius \\(R\\), the side length is \\(s = R\\sqrt{3}\\). \nWith \\(R = 2\\ \\text{m}\\),\n\n\\[\ns = 2\\sqrt{3}\\ \\text{m} \\approx 3.464\\ \\text{m}.\n\\]\n\n---\n\n**2. Radi...	[ { "role": "assistant", "content": "### Problem 1: Side Length of the Equilateral Triangle\n\nFather Kofi wants to inscribe two interlocking equilateral triangles (forming a Star of David) within a circular window of radius 2 meters. To determine the side length of each equilateral triangle, we consider ...
[ { "role": "user", "content": "Describe and explain the similarities, differences, advantages, and disadvantages of cetirizine and loratadine" } ]	[ { "role": "assistant", "content": "Cetirizine and loratadine are both second-generation, peripherally selective H₁-receptor antagonists that largely replaced older, sedating antihistamines. While they share a core mechanism and indication, their differences in metabolism, onset, and side-effect profiles cre...	[ { "role": "assistant", "content": "Cetirizine and Loratadine: A Comparative Analysis\n\nSimilarities: \n1. Pharmacological Class: Both are second-generation H1 receptor antagonists, designed to minimize sedation compared to first-generation antihistamines (e.g., diphenhydramine). \n2. Uses...
[ { "role": "user", "content": "\"AI Language model, can you describe a personal experience that you wish you could have, such as visiting a particular country, tasting a specific food or attending a certain event?\"" } ]	[ { "role": "assistant", "content": "While I don’t have personal experiences in the human sense, I can certainly imagine one I’d love to have—a spring morning in Kyoto, Japan, during cherry blossom season.\n\nI picture myself walking along the Philosopher’s Path, a canal-side stone trail lined with hundreds o...	[ { "role": "assistant", "content": "Certainly! While I don’t have personal experiences or desires, I can vividly imagine and describe a hypothetical one. For instance, I’d love to hypothetically experience a serene morning in Kyoto, Japan, during the cherry blossom season (sakura). Picture this: \n\nThe a...
[ { "role": "user", "content": "generate code for my ai saas" } ]	[ { "role": "assistant", "content": "We'll build a minimal but complete AI SaaS starter with Django (REST API + JWT auth) and React (functional components + hooks). The demo model uses scikit-learn on the 20 Newsgroups dataset – replace it with your own model later.\n\n## 1. Backend (Django)\n\n### Project st...	[ { "role": "assistant", "content": "Here's a basic code structure for an AI SaaS using Python (Django) backend with React frontend and a simple ML model. This is a simplified example to get you started:\n\n1. Backend (Django)\n```python\n# backend/ai_saaS/settings.py\nINSTALLED_APPS = [\n ...\n 're...
[{"role":"user","content":"As a skilled musician and conductor, you are tasked with arranging a spec(...TRUNCATED)	[{"role":"assistant","content":"Problem 1\n\nThe choir of 20 singers stands on a semicircle of r(...TRUNCATED)	[{"role":"assistant","content":"### 1. Angle Between Adjacent Choir Members\n\nWe are given that 20 (...TRUNCATED)

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trlm-dpo-stage-3 (synth reasoning rewrite)

Direct Preference Optimization (DPO) dataset pairing original DeepSeek-R1 distillation responses against synth-style reasoning rewrites produced by DeepSeek V4 Flash.

Source

The rejected side originates from Shekswess/trlm-dpo-stage-3-final-2. Each original assistant response followed the DeepSeek-R1-Distill style <think>...</think>\n\n<final answer> layout. For each record the <think> block was stripped of its tags to recover reasoning_content, and the trailing text after </think> was kept as content.

The chosen side was generated by DeepSeek V4 Flash, which rewrote both the reasoning (into a compact, structured "synth-like" format) and the final answer.

Format

Each record has three fields, each a list of chat messages:

{
  "prompt":   [{"role": "user", "content": "..."}],
  "chosen":   [{"role": "assistant", "reasoning_content": "<synth reasoning>", "content": "<rewritten answer>"}],
  "rejected": [{"role": "assistant", "reasoning_content": "<original reasoning>",      "content": "<original answer>"}]
}

prompt — shared user turn (identical for chosen and rejected).
chosen.reasoning_content / chosen.content — DeepSeek V4 Flash rewrite.
rejected.reasoning_content — original chain-of-thought with <think> tags removed.
rejected.content — original final answer (text after </think>).

Splits

split	examples
train	25,683
eval	1,351
total	27,034

Records are shuffled (seed 42) and split 95 / 5.

Intended use

DPO / ORPO / KTO training of reasoning models that should prefer compact, well-structured chain-of-thought over verbose unstructured reasoning.

Reasoning length: synth vs. R1

The synth rewrite is dramatically more compact than the original R1-distill reasoning. Measured on reasoning_content (tags stripped on both sides) across all 27,034 pairs:

metric	synth (chosen)	R1 (rejected)	change
mean chars	1,517	7,187	−78.9 %
median chars	1,391	3,473
mean words	232	1,239	−81.3 %
median words	214	572
p90 chars	2,322	19,014
total chars (all pairs)	41.0 million	194.3 million	−78.9 %

Synth reasoning is shorter than its R1 counterpart in 90.6 % of records (24,488 / 27,034).
Mean chosen / rejected char ratio = 0.48, median 0.39.
R1 reasoning has a very heavy right tail (max 100k chars, p90 ≈ 19k); synth reasoning stays tightly bounded (max ≈ 19k, p90 ≈ 2.3k) while preserving the structured argumentation needed for the task.

In short: the synth side teaches the model to reach the same conclusions with roughly ¼ the reasoning tokens of the R1-distill baseline.

Notes

Every original record contained exactly one <think> and one </think> tag; no malformed splits.
reasoning_content is stored without <think> tags — add them at the tokenizer/template layer if your chat template expects them.
No deduplication was applied on top of the source dataset.

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