Yunzhong He

SteerDuplex: Steerable Duplex Speech Dialogue Models

Utkarsh Tyagi, R. K. Selvakumar, Advait Gosai, Sonal Kumar et autres

Full-duplex spoken dialogue models support low-latency turn taking, interruption handling, and backchanneling, yet a key capability remains underexplored: steerability, the ability to reliably shift conversational behavior along attributes such as tone, persona, speaking rate, and voice style in response to user instructions. …

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

Harsh Raj, David Lee, Anas Mahmoud, Renxiong Wang et autres

The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it transforms outcome-level signals into actionable interventions. The sheer scale of the data renders human review impractical, driving the …

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

Harsh Raj, David Lee, Anas Mahmoud, Renxiong Wang et autres

The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it transforms outcome-level signals into actionable interventions. The sheer scale of the data renders human review impractical, driving the …

READY or Not: Reliable Enterprise Agent Deployment

Veronica Chatrath, Bryan Zhu, Jingxuan Fan, George Pu et autres

An AI agent can perform well on benchmarks and still be unsuitable for deployment. Existing AI-agent benchmarks measure whether an agent can complete realistic professional work, whereas enterprise deployment asks a different question: whether an agent can meet a required reliability level, …

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Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL

Minglai Yang, Xinyu Guo, Utkarsh Tyagi, Mian Zhang et autres

Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks with no deterministic answer. The rubric, however, is a fixed proxy for quality, never a complete description of it, …

Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures

Harsh Raj, Vipul Gupta, Anas Mahmoud, Razvan-Gabriel Dumitru et autres

Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system. This creates a repair-assignment problem: the same visible failure may call for model post-training, harness engineering, environment redesign, or benchmark …

Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures

Harsh Raj, Vipul Gupta, Anas Mahmoud, Razvan-Gabriel Dumitru et autres

Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system. This creates a repair-assignment problem: the same visible failure may call for model post-training, harness engineering, environment redesign, or benchmark …

Rubric-Guided Self-Distillation: Post-Training Without Rubric Verifiers

MohammadHossein Rezaei, Anas Mahmoud, Zihao Wang, Utkarsh Tyagi et autres

Rubrics have emerged as an alternative to RLVR in open-ended domains where a single ground-truth final answer is not available. Existing rubric-based training methods rely on an LLM verifier that scores each rollout against rubrics. This introduces substantial training-time overhead, exposes optimization …

Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR

Utkarsh Tyagi, Xingang Guo, MohammadHossein Rezaei, Daniel George et autres

Reinforcement learning with verifiable rewards has made post-training highly effective when correctness can be checked automatically. However, many important model behaviors require satisfying several qualitative criteria at once. Rubric-based rewards address this setting by grading prompt-specific criteria and aggregating them into a …

Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR

Utkarsh Tyagi, Xingang Guo, MohammadHossein Rezaei, Daniel George et autres

Reinforcement learning with verifiable rewards has made post-training highly effective when correctness can be checked automatically. However, many important model behaviors require satisfying several qualitative criteria at once. Rubric-based rewards address this setting by grading prompt-specific criteria and aggregating them into a …

Reward Hacking in Rubric-Based Reinforcement Learning

Anas Mahmoud, MohammadHossein Rezaei, Zihao Wang, Anisha Gunjal et autres

Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but …

Reward Hacking in Rubric-Based Reinforcement Learning

Anas Mahmoud, MohammadHossein Rezaei, Zihao Wang, Anisha Gunjal et autres

Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewards. We study reward hacking in rubric-based RL, where a policy is optimized against a training verifier but …