CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning
arXiv:2603.00889v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforcement learning (RL)-based post-training on high-quality reasoning data. However, reproducing and extending these capabilities in open and scalable settings is hindered by three fundamental data-centric challenges: (1) the cold-start problem, arising from the lack of seed datasets with detailed, long Chain-of-Thought (CoT) trajectories needed to initialize reasoning policies; (2) limited domain coverage, as most existing open-source reasoning datasets are concentrated in mathematics, with limited coverage of broader scientific disciplines; and (3) the annotation bottleneck, where the difficulty of frontier-level reasoning tasks makes reliable human annotation prohibitively expensive or infeasible. To address these challenges, we introduce CHIMERA, a compact synthetic reasoning dataset compris
arXiv:2603.00889v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforcement learning (RL)-based post-training on high-quality reasoning data. However, reproducing and extending these capabilities in open and scalable settings is hindered by three fundamental data-centric challenges: (1) the cold-start problem, arising from the lack of seed datasets with detailed, long Chain-of-Thought (CoT) trajectories needed to initialize reasoning policies; (2) limited domain coverage, as most existing open-source reasoning datasets are concentrated in mathematics, with limited coverage of broader scientific disciplines; and (3) the annotation bottleneck, where the difficulty of frontier-level reasoning tasks makes reliable human annotation prohibitively expensive or infeasible. To address these challenges, we introduce CHIMERA, a compact synthetic reasoning dataset comprising 9K samples for generalizable cross-domain reasoning. CHIMERA is constructed with three key properties: (1) it provides rich, long CoT reasoning trajectories synthesized by state-of-the-art reasoning models; (2) it has broad and structured coverage, spanning 8 major scientific disciplines and over 1K fine-grained topics organized via a model-generated hierarchical taxonomy; and (3) it employs a fully automated, scalable evaluation pipeline that uses strong reasoning models to cross-validate both problem validity and answer correctness. We use CHIMERA to post-train a 4B Qwen3 model. Despite the dataset's modest size, the resulting model achieves strong performance on a suite of challenging reasoning benchmarks, including GPQA-Diamond, AIME 24/25/26, HMMT 25, and Humanity's Last Exam, approaching or matching the reasoning performance of substantially larger models such as DeepSeek-R1 and Qwen3-235B.
Executive Summary
This article introduces CHIMERA, a compact synthetic reasoning dataset designed to address data-centric challenges hindering reproducibility and scalability of Large Language Models (LLMs) in open and generalizable settings. CHIMERA consists of 9K samples with rich, long Chain-of-Thought (CoT) trajectories, broad domain coverage across 8 scientific disciplines, and a fully automated evaluation pipeline. The authors demonstrate the effectiveness of CHIMERA in post-training a 4B Qwen3 model, achieving strong performance on challenging reasoning benchmarks. The study highlights the potential of synthetic data in mitigating annotation bottlenecks and improving generalizability of LLMs.
Key Points
- ▸ CHIMERA addresses data-centric challenges in LLMs, including the cold-start problem, limited domain coverage, and annotation bottleneck.
- ▸ The dataset is constructed with three key properties: rich CoT trajectories, broad and structured domain coverage, and a fully automated evaluation pipeline.
- ▸ The authors demonstrate the effectiveness of CHIMERA in post-training a 4B Qwen3 model, achieving strong performance on challenging reasoning benchmarks.
Merits
Strength in Addressing Data-Centric Challenges
CHIMERA effectively addresses the cold-start problem, limited domain coverage, and annotation bottleneck, enabling reproducibility and scalability of LLMs in open and generalizable settings.
High-Quality Synthetic Data
CHIMERA provides rich, long CoT trajectories and broad domain coverage, making it a valuable resource for LLM development and research.
Improved Generalizability of LLMs
The study demonstrates the effectiveness of CHIMERA in improving the generalizability of LLMs, enabling them to perform well on challenging reasoning benchmarks.
Demerits
Limited Scope and Generalizability
The study focuses on a specific type of LLM (Qwen3) and may not be generalizable to other architectures or domains.
Potential Overfitting to Synthetic Data
The use of synthetic data may lead to overfitting, which could compromise the performance of LLMs on real-world tasks.
Scalability and Reproducibility Concerns
The study's results may not be scalable or reproducible in larger or more diverse settings, which could limit the practical applications of CHIMERA.
Expert Commentary
CHIMERA is a significant contribution to the field of LLM research, as it addresses fundamental data-centric challenges and enables reproducibility and scalability in open and generalizable settings. However, the study's limitations, such as potential overfitting to synthetic data and scalability concerns, must be carefully considered. The use of synthetic data is a promising approach, but it requires careful evaluation and validation to ensure its effectiveness and generalizability. As the field continues to evolve, it is essential to prioritize research that addresses these challenges and enables the development of more robust and generalizable LLMs.
Recommendations
- ✓ Future research should focus on evaluating the effectiveness of CHIMERA in different LLM architectures and domains.
- ✓ The development of more robust and generalizable synthetic data generation methods is essential to address the limitations of CHIMERA and ensure its scalability and reproducibility.