SkillRL report verification

Type: agentic-system-analyses/types/agentic-system-verification.md

Verification

Evidence and disposition

The pinned reports support a bounded implementation account. The previous withdrawal blocker is repaired; no material blocker remains. The supplied structural report has no failures. Only the epistemic correction-answer input is nonempty. Its argument is supported by fresh source inspection, not accepted merely because it reports a correction.

Checkout HEAD matches the boundary's frozen revision, with no reported checkout changes. All source reads and searches below concern SRC-1 at that revision. They supply implementation evidence, except README declarations, which supply doctrine/design or reported operation. No target code, tests, services or models were executed. Identity agreement, quotation occurrence and reference resolution belong to deterministic validation; this judgment concerns meaning, conditions and strength.

Runtime and workflow

RT-RTE-environment-rollout, RT-OBJ-trajectory-batch and RT-BAP-skill-conditioning are supported by the reset, rendering, generation, projection and active-row collection chain. Independent inspection of SRC-1, agent_system/environments/env_manager.py, agent_system/multi_turn_rollout/rollout_loop.py, agent_system/memory/memory.py and agent_system/environments/env_package/alfworld/projection.py confirms reset-time selection but no initial skill delivery, positive-history gating, suffix history, single-user-message reconstruction, formatting feedback without an execution veto, calls over the whole batch and bounded termination. SRC-1, agent_system/environments/env_package/alfworld/envs.py supplies the text-mode simulator reward and Ray execution boundary. These support delivery and outcome feedback, not skill compliance or an admissible-command invariant.

RT-RTE-validation-admission and RT-RTE-training-admission have distinct supported guards and failure origins. Direct reads of SRC-1, verl/trainer/ppo/ray_trainer.py, agent_system/memory/skill_updater.py and agent_system/memory/skills_only_memory.py confirm first-occurrence validation deduplication versus row-based training inputs, failure collection and prompt caps, title-only existing-bank exposure, required-key checks, serial ID reassignment, general append and separate bank saves. Validation evidence changes training memory, not validation memory; separate managers do not imply causal isolation. Constructor failures lie outside the provider-call catch. Neither route tests candidate effectiveness before admission.

RT-RTE-policy-update, RT-CMP-actor-rollout and RT-BAP-policy-parameters match worker delegation, optimizer updates, rollout parameter transfer and checkpoint recovery in SRC-1, verl/workers/fsdp_workers.py, verl/workers/actor/dp_actor.py, verl/workers/sharding_manager/fsdp_vllm.py and verl/trainer/ppo/ray_trainer.py. The nonfinite-gradient guard is numerical, not a policy-quality veto. The reward manager's supplied-score alternative is real, SRC-1, agent_system/reward_manager/episode.py; not every configurable reward path can be attributed to simulator outcomes. Resume restores numerical and loader state, not dynamic-bank JSON. The async incompatibility qualification follows the worker selection in SRC-1, verl/trainer/main_ppo.py and rejecting synchronous method in verl/workers/fsdp_workers.py.

RT-CMP-skill-updater, RT-CMP-embedding-model and MEM-CMP-retrieval-encoder correctly distinguish remote inference, skills-only encoding and legacy goal encoding. SRC-1, agent_system/memory/skill_updater.py, agent_system/memory/skills_only_memory.py and agent_system/memory/retrieval_memory.py support their separate initialization, cache and mutability accounts. Inference and encoding do not establish fitting of these components, and deployment names/model paths do not pin exact weights.

The workflow account and its annotations preserve the shipped driver's sequence and delivery boundary. SRC-1, examples/grpo_trainer/run_alfworld_skills.sh, verl/trainer/main_ppo.py and verl/trainer/ppo/ray_trainer.py confirm the reported settings, actor fitting before scheduled validation, possible bank admission, subsequent checkpointing and terminal metrics/artifacts. Distributed workers are executors rather than autonomous collaborators. JSON persistence is not automatic deployment to another run, and checkpoint resume is not coupled instruction/policy recovery.

Memory and offline production

MEM-OBJ-skill-content, MEM-OBJ-skill-metadata and the superseded EPI-OBJ-skill-principles identify supported instruction and targeting parts. SRC-1, agent_system/memory/skills_only_memory.py, agent_system/memory/skill_updater.py and scoped bank witnesses in memory_data/alfworld/claude_style_skills.json support the text examples, loss of general applicability and mistake reasons, title-only updater hints, dynamic-first overflow, optional ranking and fixed initial existing-ID set. The serial updater guarantee is correctly narrower than universal uniqueness under arbitrary additions.

MEM-RTE-bank-maintenance is an exposed capability. List replacement and cache invalidation do not revoke dictionaries already cached for an episode. MEM-OBJ-episode-history correctly separates raw episode buffers from durable lessons and training tensors, SRC-1, agent_system/memory/memory.py and agent_system/environments/env_manager.py.

MEM-OBJ-experience-records, MEM-OBJ-retrieval-index, MEM-RTE-experience-selection and MEM-RTE-experience-save match SRC-1, agent_system/memory/retrieval_memory.py: derived records, goal encoding, model-name-only cache checking, positional indexes, alternative selection/display budgets and heuristic file saving without live memory/index refresh. Searches for saver invocations across agent_system, verl, examples and skill_generation locate declarations/documentation and the unfinished manager hook, not an automatic live producer. The afforded writer cannot be joined to the wired selector as demonstrated online learning.

RT-RTE-offline-production matches ALFWorld's categorized pattern extraction, model calls, JSON parsing and bank overwrite, SRC-1, skill_generation/alfworld.py. Empty client configuration prevents inferring usable deployment. Scoped searches of skill_generation/search.py and skill_generation/webshop.py support the alternative model/schema statements without establishing equivalent semantics. MEM-CLM-search-category correctly identifies the online task_specific_skills versus shipped query_type_skills mismatch. SRC-1, memory_data/search/claude_style_skills_search.json, skill_generation/search.py, agent_system/memory/skills_only_memory.py and examples/sft_data_generation/distillation/skill_retrieval.py distinguish that mismatch from schema-aware offline consumption.

RT-RTE-sft-production, MEM-RTE-trace-abstraction and EPI-RTE-trajectory-cleanup retain supported separate effects and containment. Direct inspection of SRC-1, examples/sft_data_generation/run_alfworld.sh, examples/sft_data_generation/preprocess/dedupe_repetitions.py, examples/sft_data_generation/skill_memory/generate_memory_alfworld.py, examples/sft_data_generation/skill_memory/aggregate_skills.py, examples/sft_data_generation/distillation/distill_alfworld.py and examples/sft_data_generation/postprocess/sharegpt_to_pairs.py confirms action-only deletion, supplied outcome labels, model abstraction, aggregation without explicit per-skill trace linkage, skill-conditioned reasoning insertion around recorded actions, synthetic terminal data and pair construction. The final pass in SRC-1, examples/sft_data_generation/postprocess/validate_and_fix_webshop.py counts admissibility mismatches without universally rejecting them. These are data transformations, not authentication or semantic acceptance.

Epistemic assessment and reconciliation

EPI-RTE-proposal-check isolates a structural gate spanning both admission routes, not a duplicate full route. EPI-OBJ-search-content, EPI-RTE-search-import and EPI-RTE-answer-check preserve the separate warrant of passages, answers and references. SRC-1, agent_system/environments/env_package/search/third_party/skyrl_gym/tools/search.py, agent_system/environments/env_package/search/third_party/skyrl_gym/envs/search/env.py and agent_system/environments/env_package/search/third_party/skyrl_gym/envs/search/utils.py confirm ordinal passage display, combined-history scoring, last answer-tag extraction and normalized reference matching. The stated normalization and cleanup deductions follow the source without execution. Scoring warrants only its comparison domain, not world truth, reasoning or skill effectiveness.

The correction to EPI-RTE-skill-withdrawal is supported. A fresh remove_skill\( search across SRC-1's agent_system, verl, examples and skill_generation finds only the definition. Direct inspection of agent_system/memory/skills_only_memory.py confirms implemented deletion/cache effects but no shipped invocation. The route now says afforded; its dependent endpoint, read-back, activation and bounded-conclusion wording distinguish a callable method from an integrated workflow. The ledger's architectural implemented status describes the method, not stronger wiring. This resolves the previous blocker without erasing the supported capability or cached-selection limits.

RT-CLM-reusable-patterns, EPI-CLM-recursive-evolution and EPI-CLM-context-efficiency accurately attribute separate claims in SRC-1, README.md. The ledger and bounded conclusion do not promote those declarations into observed or causal benefit. Candidate generation, checking, operational admission, retention and adaptation remain distinct. Unavailable generated instances justify indeterminate semantic edges. Formulation, actual content dependence, criticism, criticism-driven iteration, learning, reflection, autonomy and improvement remain independent; implemented delivery is not exercised dependence.

The reconciliation preserves these findings and all memory integration issues. Its single skill-content supersession has matching referent evidence and retains both declarations. The two explicit SFT Part of: relations are supported; cleanup is a stage overlapping abstraction, not a duplicate whole container. Proposal checking spans two routes and appropriately keeps that comparison rather than claiming exactly one parent. Withdrawal is narrower than the maintenance container. Distinct encoders, targeting metadata, experiences, buffers, search content and training batches are not conflated. No evidenced-absence declarations require disposition. The former withdrawal disagreement is now resolved in the supplied reports; no unsettled cross-report conflict was found. The reconciliation's statement that no correction answers were supplied to its own attempt does not deny the answer supplied to this verification.

Omission questions and stopping decision

The frozen top-level tree, scoped tracked memory/SFT/generator tree and producer/consumer dependencies guided three questions: whether withdrawal or experience saving has an overlooked shipped caller; whether alternate selection/delivery defeats uniform skill-context claims; and whether offline downstream stages introduce an omitted semantic acceptance or provenance check. Fresh caller searches support capability-only withdrawal and saving. Selector, manager and schema checks support the Search mismatch and the WebShop fallback below. ALFWorld SFT driver, aggregation, distillation, flattening and WebShop final-pass checks support construction rather than verified semantics. The shipped training-update driver selects the already-declared alternate admission route, SRC-1, examples/grpo_trainer/run_alfworld_skills_train_update.sh.

The WebShop delivery qualification is retained from direct inspection, not assumed from the previous verification:

        if len(obs) > 13000:
            print(f"Warning len(obs)={len(obs)} is too long")
            obs = WEBSHOP_TEMPLATE_NO_HIS.format(
                task_description=self.tasks[i],
                current_observation=text_obs[i],
                available_actions=reformatted_available_actions
            )

--- agent_system/environments/env_manager.py

Further optional omission expansion stops because the identified material challenges have supported dispositions. Remaining environment evaluators, provider/service internals, alternate optimizers, parsing and earlier WebShop repairs cannot support uniform safety, evaluator, semantic-preservation or recovery comparisons; the reports already withhold those comparisons. Their availability alone does not require a framework-wide audit to retain this bounded training/skill-loop account.

Blockers

none

Limits

• The alternate prompt-delivery inventory remains incomplete for WebShop. MEM-OBJ-skill-content, the memory annotation on RT-RTE-environment-rollout and MEM-RTE-experience-selection establish selection and possible later guidance, not uniform retention in the final prompt. SRC-1, agent_system/environments/env_manager.py replaces a WebShop observation longer than 13,000 characters with its no-history template, discarding that invocation's history and selected skills. This does not invalidate the ALFWorld-centered runtime/workflow or the memory report's explicit uninspected WebShop length-handling qualification. Withhold cross-manager delivery-preservation/context-eviction comparisons and any inference that selecting a retained WebShop rule guarantees inclusion. No observed fallback or successful rule influence is supplied.