EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable
  Learning
Reliable verifiable data has become a key driver of capability gains in
modern language models, enabling stable reinforcement learning with verifiable
rewards and effective distillation that transfers competence across math,
coding, and agentic tasks. Yet constructing generalizable synthetic verifiable
data remains difficult due to hallucination-prone generation, and weak or
trivial verification artifacts that fail to separate strong from weak
solutions. Existing approaches often rely on task-specific heuristics or
post-hoc filters that do not transfer across domains and lack a principled,
universal evaluator of verifiability. In this work, we introduce an
evolutionary, task-agnostic, strategy-guided, executably-checkable data
synthesis framework that, from minimal seed supervision, jointly synthesizes
problems, diverse candidate solutions, and verification artifacts, and
iteratively discovers strategies via a consistency-based evaluator that
enforces agreement between human-annotated and strategy-induced checks. This
pipeline upgrades filtering into principled synthesis: it reliably assembles
coherent, verifiable training instances and generalizes without domain-specific
rules. Our experiments demonstrate the effectiveness of the proposed approach
under both RLVR and model distillation training paradigms. The results show
that training with our synthesized data yields significant improvements on both
the LiveCodeBench and AgentBench-OS tasks, highlighting the robust
generalization of our framework.
68f84e447669bcaeecce0d5f
He Du
claimed_verified
2025-10-22T15:43:32.696Z
64ec5ffec782d648d2909c68
/avatars/297f61af080206d88d68f18fd96c9aa3.svg
duhe
user
Elynden
68f84e447669bcaeecce0d60
Bowen Li
68f84e447669bcaeecce0d61
Aijun Yang
68f84e447669bcaeecce0d62
Siyang He
68f84e447669bcaeecce0d63
Qipeng Guo
68f84e447669bcaeecce0d64
Dacheng Tao
EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable
  Learning
Reliable verifiable data has become a key driver of capability gains in
modern language models, enabling stable reinforcement learning with verifiable
rewards and effective distillation that transfers competence across math,
coding, and agentic tasks. Yet constructing generalizable synthetic verifiable
data remains difficult due to hallucination-prone generation, and weak or
trivial verification artifacts that fail to separate strong from weak
solutions. Existing approaches often rely on task-specific heuristics or
post-hoc filters that do not transfer across domains and lack a principled,
universal evaluator of verifiability. In this work, we introduce an
evolutionary, task-agnostic, strategy-guided, executably-checkable data
synthesis framework that, from minimal seed supervision, jointly synthesizes
problems, diverse candidate solutions, and verification artifacts, and
iteratively discovers strategies via a consistency-based evaluator that
enforces agreement between human-annotated and strategy-induced checks. This
pipeline upgrades filtering into principled synthesis: it reliably assembles
coherent, verifiable training instances and generalizes without domain-specific
rules. Our experiments demonstrate the effectiveness of the proposed approach
under both RLVR and model distillation training paradigms. The results show
that training with our synthesized data yields significant improvements on both
the LiveCodeBench and AgentBench-OS tasks, highlighting the robust
generalization of our framework.
An evolutionary framework synthesizes verifiable data for language models, improving reinforcement learning and distillation across various tasks.
reinforcement learning verifiable rewards distillation synthetic verifiable data hallucination-prone generation verification artifacts task-agnostic strategy-guided executably-checkable consistency-based evaluator RLVR LiveCodeBench AgentBench-OS