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River executes remote sampling, autograd, and optimizer work. StateSet supplies task definitions, rewards, batch/protocol checks, selection, and retained experiment evidence. The reviewed integration supports Python 3.12+ and river-client>=0.11.0,<0.12. From the reviewed source checkout:
Configure RIVER_API_KEY through your environment for live use. Local preflight checks SDK, Python, and credential presence without constructing a client or making a provider request. --live separately queries account model capabilities; neither mode trains or samples. A passing capability report does not establish funding, capacity, or model quality.

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Choose the actual workflow

The studio’s River backend supports SFT, not native studio GRPO/GSPO/DAPO. These workflows have different data and recovery contracts. A nominal algorithm name does not establish that the same trainer, reward, or optimizer schedule was used.

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Managed SFT and hosted artifacts

The studio requires explicit cost_policy: provider_billed and an authorized remote launch with --allow-paid. This interface does not enforce a dollar ceiling or a River whole-run deadline. Token reservations and request timeouts are not dollar-spend guarantees. SFT captures dataset source bytes/row identity and starting-checkpoint lineage before work. For continuation, a RemoteJobSpec can explicitly set harvest.checkpoint to a river:// URI or a directory with river_checkpoint.json. Declared model/rank compatibility is checked before provider work. parent_adapter records lineage separately; it does not load weights. Results retain river_checkpoint.json, a river:// URI, and provenance. The file is a hosted pointer, not downloaded adapter weights, so local serve --checkpoint cannot load it. Hosted inference/deployment has its own account capability and billable lifecycle.

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Native refund RL

Inspect the runner and prepare its default offline plan from the source checkout:
The native runner bridges StateSet environments into River’s rollout engine. Policies must execute sandbox lookups/actions to obtain private facts; prompt text is not evidence that an external action occurred. Successful sandbox episodes are separate from real refunds, bank transfers, or merchant outcomes. Current evidence binds generated token IDs, sampled log probabilities, rewards, masks, native datums, checkpoint receipts, and acknowledged updates. Invalid sampling evidence or policy mismatch rejects a batch before forward/backward. Truncation is explicit and cannot silently become a successful episode or verified SFT demonstration. The latest recovery replication used one logical CISPO batch per seed: group-centered advantages without standardization, token normalization, four samples per task, LoRA rank 16, and at most one acknowledged update. This is the retained study’s configuration, not a universal RL default. Zero-signal attempts are retained rather than replaced until a favorable batch appears.

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Recovery and provider uncertainty

Synchronous RL persists validated rl_state.json and optimizer checkpoint progress. Explicit resume restores the last committed state and executes only remaining rounds. If all rounds committed, final local publication can finish offline. Missing/corrupt state blocks continuation instead of restarting fresh. An in-flight remote update can outlive client interruption. Local receipts cannot undo it, refund its usage, or independently attest remote weight bytes. Preserve the original configuration/handle and inspect committed progress before retrying. Cleanup errors retain the original training outcome and diagnostics; a completed checkpoint can remain fetchable even when client cleanup fails. Native recovery and study admission use their separate retained-state protocol. Do not substitute the local studio checkpoint flow for River-hosted state.

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Latest retained results

The recovery RL test difference was 0.625 percentage points, with exact two-sided seed sign-flip p = 0.125 and seed/case bootstrap 95% interval [0, 1.458] percentage points. These observations condition on 40 shared synthetic case clusters and six training seeds; four rollouts per case are aggregated before inference. They are not 960 independent merchant trials or six independent base-model/SFT-parent replications. An initial consecutive-seed protocol was stopped after discovering deterministic sampler-stream reuse. It submitted zero optimizer updates and no evaluation cases; its admissions/cost uncertainty remain separate. The corrected protocol retained a source-bound sampler audit and widely spaced seeds. Distinct seed identities prevent direct reuse under that audit; they do not prove random-stream independence. The final study retained no observed test regressions but failed its significance and prespecified strict per-seed improvement requirements. No best seed or checkpoint was promoted. Billed dollar cost is unavailable; reserved tokens are admission bounds rather than actual usage or a dollar cap. Production quality remains unverified.

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Reproduce the review boundary

The evidence source is the local October 20, 2026 benchmark_results/training_contracts/20261020/river-recovery-replication/README.md and replication-statistics.json, with the stopped protocol retained separately. The provider contract is docs/RIVER_PROVIDER.md; the arithmetic and boundary studies have their own dated reports. Keep their original protocol, failures, and unattempted holdouts when reporting results. Current native SDK CI uses the real River rollout engine/trainer with scripted transport and forbids live client/RPC construction. Those offline contract checks are separate from paid provider evidence. No live training, deployment, or model-quality claim follows merely from a passing mock suite. Continue with data isolation or checkpoint selection and quality evidence.
Last modified on October 20, 2026