| # StarVLA-RSI agent instructions |
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| This repository evaluates complete agent methods, not only bare model names, |
| and is not a model-training repository. Participants may compose multiple |
| general-purpose models and own their planning, memory, tools and decision loop. |
| Direct control limits environment permissions and robot execution interfaces, |
| not internal reasoning. Protocol-first integration is primary; Python Policy and |
| the reference loop are optional. Keep method/model roles and configuration |
| explicit; report measured usage without inventing missing attribution. |
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| Preserve native benchmark execution budgets. Existing method/track safety caps |
| remain declared, not universal limits on inference. Interaction/media recording |
| is optional, default enabled, and never a publication prerequisite. Essential |
| results, denominators, counters, configuration and errors survive minimal mode. |
| Retention options must not change actor inputs, controls, lifecycle or scoring; |
| runtime scratch inputs live until episode closure. Internal hashes/source audits |
| remain support tools, separate from public result eligibility. |
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| Action chunk length, repeated controls per decision and replan timing belong to |
| the policy method and its trusted driver, not a universal server-side prefix. |
| The current service accepts one action per sequenced `step`; the driver may send |
| successive controls from one decision, subject to action validity, native control, |
| termination and the declared total execution budget. Native physics substeps and |
| protocol-fixed interpolation are not policy-adjustable repeats. Legacy |
| decision-budget tracks still couple call/prefix settings to execution caps, and |
| some interfaces declare `required_replan_interval`; preserve those contracts |
| until an explicit migration. Do not claim all existing tracks are decoupled. |
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| The product scope is direct control by general-purpose models across many |
| benchmarks. Do not add VLA-assisted/hybrid control, learned action proposals, |
| fallback task policies, or automatic task-solving skill services. The model |
| must select the actions through the declared observation-to-decision interface. |
| Only pinned native low-level control and protocol-fixed action conversion may |
| execute them; they must not plan the task for the model. External reports are |
| design references, not permission to expand observations or control capabilities. |
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| Use a multi-agent implementation -> independent review -> regression validation |
| -> correction loop for framework changes. Assign non-overlapping implementation |
| files, review cross-layer effects independently, and test a stable source snapshot |
| before applying changes to the evaluation checkout. A passing unit test is not |
| evidence of native evaluation, provider attestation or complete benchmark parity. |
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| Before changing evaluation code or protocols: |
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| 1. Read `docs/architecture.md` and `docs/evaluation.md`. Internal |
| `project_readmes/` notes, if present, are local context, not required public docs. |
| 2. Read the target benchmark's `EVALUATION_PROTOCOL.md` and its native evaluator. |
| 3. Treat the pinned upstream evaluator as the source of truth. Do not guess action |
| semantics, success criteria, episode counts, seeds, or observation layouts. |
| 4. Record verified evidence separately from migration TODOs. |
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| New benchmark integrations must use the contracts under `benchkit/`, |
| delegate task success to the native benchmark, and add a dependency-light test. |
| Training recipes and dataset registries do not belong under |
| `benchmarks/simBenchmarks`. |
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| Evaluation artifacts must identify the benchmark and protocol versions, policy |
| and model, seed and episode, action contract, source revision, token/cost/latency |
| usage when available, and all errors. Infrastructure errors must not be silently |
| counted as policy failures. |
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| The primary control architecture has two ends, not a mandatory harness middle |
| layer. Model-specific trusted evaluation entrypoints belong under |
| `AgentPolicies/<profile>/`; reusable agent-side decision loops belong under |
| `AgentPolicies/policy_support/`. Benchmark adapters, native runtime/asset helpers, |
| task inventories and evaluation plans belong beside their protocols under |
| `benchmarks/simBenchmarks/<benchmark>/` (future hardware adapters under |
| `benchmarks/realRobots/`); shared SIM transport lives in |
| `benchkit/sim_server/`, which owns reusable communication and lifecycle mechanics, |
| not model decisions or native task semantics. `benchkit/` also supplies result/data |
| formats, recording, source capture, audits and reporting tools. Fixed-plan |
| assembly belongs in `benchkit/campaigns`, report import/coverage tools in |
| `benchkit/reports`, and benchmark metadata in `benchkit/catalog`. Relocated modules |
| do not keep old-entrypoint forwarding shells. Import the optional runner directly |
| from `AgentPolicies.policy_support.runner`. Do not rewrite frozen old packages. |
| Benchmark code owns task selection and native success; the model owns decisions. |
| Central task scheduling is retained in this version: coordinating fixed plans, |
| case claims, launches and progress is distinct from making robot decisions. |
| Current campaign commands use their frozen local execution path; do not describe |
| them as remote-service orchestration or distributed recovery. Preserve the |
| single-attempt and uncertain-ACK/no-replay rules when extending scheduling. |
| Plugin discovery, a comprehensive unified CLI and cross-benchmark leaderboards |
| are long-term goals, not requirements to build in every cleanup iteration. |
| An explicitly reviewed trusted agent driver may hold the administrative SIM |
| client for fixed claims and result collection. The model actor must receive |
| only declared observations/counters, never that client, its credentials or |
| management/scoring methods. An administrative client is not actor-safe merely |
| because its file moved to another directory. No native simulator imports in |
| model drivers or policies. |
| Shared fixed-action infrastructure fixtures belong under `benchkit/diagnostics/`, |
| not `AgentPolicies/`. They may implement `Policy` only for explicit non-capability |
| diagnostics; no model inference or task-solving strategy belongs there. |
| Benchmark-specific native readiness tools remain under its `environment/`. |
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| Model-specific policy code/configuration and evaluation results belong under |
| `AgentPolicies/<model-profile>/`; shared policy behavior belongs under |
| `AgentPolicies/policy_support/`. New reports, coverage lists and videos use the model's |
| `results/<benchmark>/` namespace. Historical anonymous trials must not be renamed |
| as verified named-model scores. Preserve frozen original campaigns and result |
| bytes; relocation indexes and byte-checked presentation copies are allowed. |
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| User-facing rollout export directory names must include the canonical task ID |
| and a short English task description, in addition to date/round/case identifiers. |
| Do not use only names such as `2026-09-07-round2-case000`. For example: |
| `2026-09-07-stack_bowls-bowl-stacking-round2-case000/video_cam_head.mp4`. |
| Keep camera identity in video filenames. New video exports go under the model's |
| result namespace, not examples; old examples paths may retain compatibility links. |
| Separate reset-only and no-policy |
| diagnostic exports under `rollouts/archive/` and label their purpose. Never |
| rename frozen campaign identities or overwrite original results to tidy exports; |
| record relocations in the rollout README and preserve original artifact bytes. |
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| Local commits are allowed. Never run `git push` or otherwise change remote Git |
| state. |
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| Persistence and voluntary give-up rules are policy behavior, not benchmark |
| rules. A policy may explicitly configure a minimum number of executed action |
| decisions in its own versioned configuration/prompt; the shared harness only |
| records execution feedback and enforces declared public hard limits. Never |
| silently turn one model's persistence setting into a requirement on all models. |
| Native success/termination, exhausted limits, errors and explicit safety stops |
| must not be delayed to meet a policy's persistence target. Historical runs retain |
| their original frozen implementation and results; an old policy rule neither |
| changes benchmark definitions nor authorizes another attempt. |
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| `fast` means a preselected subset with one attempt per selected task. `full` |
| means the native protocol's declared task/variant/seed/episode schedule, not |
| success-seeking retries. Both retain failures and errors. Full presets are not |
| yet implemented for all benchmarks: never label task-only coverage as native |
| full evaluation or guess its episode count. Refactoring never starts an evaluation. |
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