Envs-FORGE: Frontier-Optimized Reward-Grounded Environment Synthesis for Agent RL
Reinforcement learning (RL) for terminal agents needs executable training environments with reliable rewards and useful difficulty. Fixed recipes such as few-shot, Self-Instruct, and Evol-Instruct apply the same prompting policy to every seed, even when the current policy would benefit from a harder, easier, or simply different task. We present Envs-FORGE, a prompting policy that converts verifier rewards into per-seed environment-synthesis actions. Envs-FORGE estimates seed pass rates, scores six projection--direction actions around a target learning frontier, and solves a per-seed mixed-inte
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- FuzzySimilar title/name (fuzzy) · 87%CodeForge-15B →
“Fuzzy title match (0.94): “Envs-FORGE: Frontier-Optimized Reward-Grounded Environment S” ≈ “CodeForge-15B””
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “Envs-FORGE: Frontier-Optimized Reward-Grounded Environment S” ≈ “AgentCore-8B””
- FuzzySimilar title/name (fuzzy) · 87%SWE-agent/SWE-agent →
“Fuzzy title match (0.94): “Envs-FORGE: Frontier-Optimized Reward-Grounded Environment S” ≈ “SWE-agent/SWE-agent””
- FuzzySimilar title/name (fuzzy) · 87%zhayujie/CowAgent →
“Fuzzy title match (0.94): “Envs-FORGE: Frontier-Optimized Reward-Grounded Environment S” ≈ “zhayujie/CowAgent””
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Xiaojun Wu →
“Envs-FORGE: Frontier-Optimized Reward-Grounded Environment Synthesis for Agent RL”