# Frequently Asked Questions ## Which verifier backends are supported? The continuous reward is the expectation over the verifier's **token-level logprobs** at the `` / `` positions, so the backend must expose logprobs. Two backends are picked automatically from the environment: - **OpenAI-compatible server** (vLLM / SGLang / OpenAI) when `OPENAI_BASE_URL` is set — e.g. `vllm serve Qwen/Qwen3.5-9B` and `export OPENAI_BASE_URL=http://localhost:8000/v1`. The served model is auto-detected, so no `model=` argument is needed. - **Gemini via Vertex AI** otherwise, from `VERTEX_API_KEY` (logprob extraction for Gemini requires the Vertex API). You can also pass your own pre-built `openai` or `google-genai` client via the `client` argument. ## Can I use a local open model as the verifier? Yes — serve it with vLLM (or SGLang) and point `OPENAI_BASE_URL` at it; see [Set up a verifier backend](../get_started/installation.md#set-up-a-verifier-backend). On this backend the score tags are **prefilled** and the score position is constrained to the 20 scale letters via structured outputs, so the extracted distribution stays calibrated even for models that don't reliably follow the tag format. ## Can I use GPT or Claude as the verifier? Not directly — those APIs withhold token-level logprobs, which the continuous reward requires. Use the two-stage workaround described in [Logit-Restricted Frontier Models](../advanced_features/logit_restricted_models.md): the closed model supplies the reasoning, and an open verifier (e.g., Gemini 2.5 Flash) supplies the calibrated distribution. On Terminal-Bench V2 this recovers a +5.2-point gain over the closed model's integer scores. ## How is this different from LLM-as-a-Judge? A judge collapses its belief into one discrete score, which produces ties on complex solutions (27% ties on Terminal-Bench V2 with coarse scoring). The verifier keeps the full distribution over score tokens and takes its expectation, yielding continuous scores with **zero ties**, and scales further along granularity, repeated evaluation, and criteria decomposition. See [Verification as a Scaling Axis](../advanced_features/verification_scaling.md). ## How much does a `select` call cost? `O(Nk)` directed comparisons instead of `O(N²)`, each costing `C × K` verifier calls (criteria × `n_evaluations`) — check `result.n_comparisons` after a run. Reduce cost with fewer `pivots`, fewer `n_evaluations`, or a score `cache`. ## How do I make runs reproducible and resumable? - **Reproducible**: identical inputs with the same `seed` run the identical tournament. - **Resumable**: pass `cache="path/to/cache.json"`. Every scored `(criterion, task, A, B, repeat)` tuple is cached; re-runs only score comparisons not seen before. Error ties are never persisted to the cache. ## What happens when a verifier call fails? In `select`, the default `on_error="tie"` scores that comparison 0.5/0.5 for the current run only; pass `on_error="raise"` to fail fast. `compare` and `track` always raise. ## Should I use `track` or `ProgressTracker`? Use `track` for finished trajectories (one call per repeat, cheapest). Use `ProgressTracker` when the agent is still running and you need scores that structurally cannot peek at the future — e.g., to abandon a hopeless rollout early. See [Progress Tracking](../basic_usage/progress_tracking.md). ## My trajectories are very long. What should I do? Steps and trajectories are plain strings, so truncate long tool observations yourself before passing them in. For best-of-N selection, decomposed criteria (see [Writing Verifier Criteria](../basic_usage/criteria.md)) also help the verifier focus on the relevant evidence. ## Does the verifier work outside of coding? Yes — the same framework is applied zero-shot to robotics (RoboRewardBench, 87.4% with a Qwen 3.6 35B VLM) and medical agent tasks (MedAgentBench, 73.3%), and as a dense RL reward on LIBERO and MATH. See [Benchmark Results](../benchmarks/results.md).