Reward Stress Tests for Agentic Post-Training with Ray
summary
Describes a Ray-backed workflow that stress-tests reward pipelines before scaling RL: synthetic injection of ~4,600 mechanical mutations yields ~1,000 validated tasks, running ~15k sandbox executions and 3k+ rollouts to find failure modes. Hardened scorer isolation cuts false positives from 98% to 67% and turning it off yields ~24% false positives in samples; distributed rollout sweeps and paired evaluations reveal best-of-n selection amplifies broken verifiers, while trajectory judges catch ~73% of process hacks; code at github.com/RGaonkar/ray-agent-reward-lab.
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chapters (8)
- 0:00Introduction
- 1:52Reward integrity problem
- 6:10Failure modes in verification
- 13:55Synthetic task validation
- 21:42Process-level reward hacking
- 27:42Trajectory-level analysis
- 30:31Architecture and scalability
- 32:00Stress testing workflow
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