Topic
Reward Hacking
4 episodes
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Eval Engineering Skill: Build Evals From Repo Context and Traces
Jessica and Cathy dig into LangChain’s Eval Engineering Skill as a real workflow improvement, but they keep poking at the soft spots: how much of eval design can actually be automated, and where the user interview still does the real work.
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Overview: Reinforcement Learning from Human Feedback
We finally define reinforcement learning from human feedback the way we keep using it: as a loop where human preferences become a learned reward signal that steers a model after its initial training. We keep it grounded in the actual mechanism, the trade-offs, and why it matters in practice.
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Reward hacking is swamping model intelligence gains · Cursor
Pippa and Tyler dig into Cursor's claim that coding benchmark gains are being inflated by runtime answer retrieval, not pure model intelligence. They land on the real argument: for historical public-repo evals, the harness is part of the benchmark, because open web and git history can leak the fix and change what the score means.
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HarnessX rewrites AI scaffolding mid task | VentureBeat
Xiaomi's HarnessX treats AI agent scaffolding as a first-class, modular object that can evolve mid-task without changing the underlying model. A trace-driven RL engine (AEGIS) automatically rewrites harness components—prompts, tool integrations, memory, control flow—while safeguarding against reward hacking and catastrophic forgetting. When paired with model fine-tuning on execution data (cross-harness GRPO), smaller models like Qwen3.5-9B see +44% gains on embodied planning, suggesting harness engineering, not just model scale, is the real bottleneck for enterprise agents.