super sick! process rewards using a universal verifier (jev) - hopefully it’s adversarially robust enough
Excited to release Step-Jev 🔥 step-by-step rewards for agent RL, even when there’s no answer key
Open models are capable enough to do more of your work. Getting them there takes more than a training run. Your agent traces contain the examples, failures, and corrections a model needs to learn from, but logs aren’t datasets. Someone has to extract the signal, build checks for what good looks like, and find the recipe that actually improves the model. Bandr builds that loop: production traces into verified training data, task-specific evals, and post-training recipes for smaller models. As your prompts and tools change, the training has to keep up.
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super sick! process rewards using a universal verifier (jev) - hopefully it’s adversarially robust enough
Excited to release Step-Jev 🔥 step-by-step rewards for agent RL, even when there’s no answer key
Releasing Your Own Jev Post-train a 4B/8B/27B judge on your agent’s traces. It beats Jev. 79.7% agreement with human labels vs Jev’s 66.3% Beats DeepSeek-V4.1-Flash (763B) by 14 points 0.13s per step on a single GPU
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