Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
Anisha Gunjal · Anthony Wang · Elaine Lau · Vaskar Nath · Yunzhong He · Bing Liu · Sean Hendryx
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Anisha Gunjal · Anthony Wang · Elaine Lau · Vaskar Nath · Yunzhong He · Bing Liu · Sean Hendryx
Mohit Raghavendra · Anisha Gunjal · Bing Liu · Yunzhong He
Verification is critical for improving agents: it provides the reward signal for Reinforcement Learning and enables inference-time gains through Test-Time Scaling (TTS). Despite its importance, verification in software engineering (SWE) agent settings often relies on code execution, which can be...
Afra Feyza Akyürek · Advait Gosai · Chen Bo Calvin Zhang · Vipul Gupta · Jaehwan Jeong · Anisha Gunjal · Tahseen Rabbani · Maria Mazzone · David Randolph IV · Mohammad Mahmoudi Meymand · Gurshaan Chattha · Paula Rodriguez · Diego A. Mares Buendia · Pavit Singh · Michael Liu · Subodh Chawla · Peter Cline · Lucy Ogaz · Ernesto Gabriel Hernández Montoya · Zihao Wang · Pavi Bhatter · Marcos Ayestaran · Bing Liu · Yunzhong He
Frontier model progress is often measured using academic benchmarks that provide a limited view of performance on open-ended, economically consequential tasks in high-stakes professional domains where practical returns matter most. We introduce Professional Reasoning Bench (PRBench), a realistic,...