Powering Verifiable Learning via Automated Evolutionary Data Synthesis (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to building generalizable verifiable data are task-specific and lack a principled, universal evaluator of verifikatability. |
| Approach: | They propose a task-agnostic, strategy-guided, executably-checkable data synthesis framework that synthesizes problems, diverse candidate solutions and verification artifacts from a single source. |
| Outcome: | The proposed framework synthesizes problems, candidates, and verification artifacts from human-annotated and strategy-induced checks and iteratively discovers strategies. |
Similar Papers
Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)
Copied to clipboard
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck. |
| Approach: | They propose a framework that uses a generative verifier to provide soft, probabilistic rewards. |
| Outcome: | The proposed framework outperforms existing models up to 10x their size and can be scalable and effective. |
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for generating high-quality reasoning data are limited in quality and availability. |
| Approach: | They propose a method that constructs mathematical operations and generates verifiable graphs that are back-translated into complex problems. |
| Outcome: | The proposed method achieves a 6.3% performance gain over existing methods on LLaMA-3-8B and outperforms others with only half the training data (50k vs. 100k). |
KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing code-focused resources typically fail to ensure either the breadth of coverage or verifiable correctness. |
| Approach: | They propose a synthetic dataset that provides high-quality, verifiable training data for Large Language Models for coding. |
| Outcome: | The proposed dataset surpasses Qwen2.5-Coder-32B-Instruct and DeepSeek-R1-Distill-Llama-70B in performance on coding benchmarks. |
MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis (2026.findings-acl)
Copied to clipboard
| Challenge: | Current approaches to synthesising high-quality mathematical reasoning data without human priors suffer from mode collapse and limited logical complexity. |
| Approach: | They propose a hierarchical synthesis framework that formulates data synthesis as an unsupervised optimization problem over a constraint graph followed by semantic instantiation rather than a direct text generation task. |
| Outcome: | The proposed framework outperforms widely-used datasets on eight mathematical benchmarks. |
SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis (2026.findings-acl)
Copied to clipboard
| Challenge: | SynthRL synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples. |
| Approach: | They propose a scalable and guaranteed pipeline for automatic data scaling in reasoning-oriented RL training. |
| Outcome: | The proposed pipeline synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples. |
PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets with verifiable answers are limited in reliability, diversity, and scalability . a new approach to generate verifikatable data at scale is needed to improve models' performance . |
| Approach: | They propose a formal framework for synthesizing verifiable data at scale using a novel DSL-driven approach. |
| Outcome: | The proposed framework improves performance on a wide range of puzzles and logic benchmarks. |
Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026.acl-long)
Copied to clipboard
Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, Nanqing Dong
| Challenge: | Recent large language models (LLMs) have demonstrated remarkable progress in reasoning, but their applications on knowledge-intensive domains have not been explored due to the scarcity of high-quality verifiable data. |
| Approach: | They propose a framework that extends reinforcement learning with verifiable rewards (RLVR) to knowledge-intensive domains through automated verififiability data synthesis while enabling verification of the LLM's reasoning process. |
| Outcome: | Extensive experiments show that the proposed framework enhances the reasoning of large language models in knowledge-intensive domains without significantly compromising the model’s general capabilities. |
VerIF: Verification Engineering for Reinforcement Learning in Instruction Following (2025.emnlp-main)
Copied to clipboard
| Challenge: | Best practices for RL in instruction following remain underexplored. |
| Approach: | They propose a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model. |
| Outcome: | The proposed method achieves state-of-the-art performance among models of comparable size and generalizes well to unseen constraints. |
CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for generating instruction-code pairs rely on rigid heuristics and are labor-intensive. |
| Approach: | They propose a dual-agent architecture that integrates a Coder and a Reviewer to orchestrate the generation trajectory. |
| Outcome: | The proposed architecture outperforms baselines on a large-scale dataset of instruction-code pairs with stepped difficulty levels. |
TTVS: Boosting Self-Exploring Reinforcement Learning via Test-time Variational Synthesis (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing test-time methods are limited in specialized or novel domains where supervision is prohibitively expensive or unavailable. |
| Approach: | They propose a framework that augments training stream from unlabeled test queries. |
| Outcome: | Extensive experiments show TTVS outperforms state-of-the-art RL-based techniques on unlabeled test-time data. |