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classical-rl-efficiency-traces

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
agent_config_sha256: string
call_models: list<item: string>
  child 0, item: string
campaign_arm_id: string
capability_reward: double
checkpoint_grades_present: bool
claim_fraction: double
collector_status: string
configured_runtime: struct<allow: list<item: null>, block: list<item: string>, cpu: double, creates_per_min: int64, disk (... 147 chars omitted)
  child 0, allow: list<item: null>
      child 0, item: null
  child 1, block: list<item: string>
      child 0, item: string
  child 2, cpu: double
  child 3, creates_per_min: int64
  child 4, disk: double
  child 5, guaranteed: bool
  child 6, idle_timeout: double
  child 7, image: string
  child 8, labels: list<item: null>
      child 0, item: null
  child 9, memory: double
  child 10, type: string
  child 11, vm: bool
  child 12, workdir: string
contract_coverage_reward: double
environment_archive_sha256: string
error_types: list<item: string>
  child 0, item: string
first_source_id: string
grader_sha256: string
harness_id: string
harness_version: string
has_recorded_errors: bool
identity_status: string
identity_variant_count: int64
inference_provenance: struct<cache_precision: null, checkpoint_revision: null, compaction_policy: string, quantization: nu (... 58 chars omitted)
  child 0, cache_precision: null
  child 1, checkpoint_revision: null
  child 2, compaction_policy: string
  child 3, quantization: null
  child 4, serving_implementation: string
  child 5, serving_version: null
is_canonical_identity_record: bool
i
...
pu: double
  child 4, creates_per_min: int64
  child 5, disk: double
  child 6, guaranteed: bool
  child 7, id: string
  child 8, idle_timeout: double
  child 9, image: string
  child 10, image_cached: bool
  child 11, labels: list<item: null>
      child 0, item: null
  child 12, memory: double
  child 13, type: string
  child 14, vm: bool
  child 15, workdir: string
recorded_status: string
reference_equivalence: double
rl_diagnostics_status: string
run_id: string
sampling: struct<max_tokens: int64, temperature: double, reasoning_effort: string>
  child 0, max_tokens: int64
  child 1, temperature: double
  child 2, reasoning_effort: string
source_lines: double
source_roles: list<item: string>
  child 0, item: string
started_at_utc: string
stop_condition: string
strict_package_reward: double
task: string
task_category: string
task_difficulty: string
task_wire_sha256: string
telemetry_schema_version: string
tests_archive_sha256: string
trace_id: string
trace_ok: bool
trace_schema_version: int64
trajectory_event_count: int64
usage_calls_present: int64
usage_fields_complete: struct<cached_input_tokens: bool, completion_tokens: bool, cost: bool, prompt_tokens: bool, reasonin (... 15 chars omitted)
  child 0, cached_input_tokens: bool
  child 1, completion_tokens: bool
  child 2, cost: bool
  child 3, prompt_tokens: bool
  child 4, reasoning_tokens: bool
verifiers_version: string
bytes: int64
indexed: bool
mtime: double
duplicate_name: bool
archive_source_id: string
member: string
to
{'archive_source_id': Value('string'), 'bytes': Value('int64'), 'duplicate_name': Value('bool'), 'indexed': Value('bool'), 'member': Value('string'), 'mtime': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              agent_config_sha256: string
              call_models: list<item: string>
                child 0, item: string
              campaign_arm_id: string
              capability_reward: double
              checkpoint_grades_present: bool
              claim_fraction: double
              collector_status: string
              configured_runtime: struct<allow: list<item: null>, block: list<item: string>, cpu: double, creates_per_min: int64, disk (... 147 chars omitted)
                child 0, allow: list<item: null>
                    child 0, item: null
                child 1, block: list<item: string>
                    child 0, item: string
                child 2, cpu: double
                child 3, creates_per_min: int64
                child 4, disk: double
                child 5, guaranteed: bool
                child 6, idle_timeout: double
                child 7, image: string
                child 8, labels: list<item: null>
                    child 0, item: null
                child 9, memory: double
                child 10, type: string
                child 11, vm: bool
                child 12, workdir: string
              contract_coverage_reward: double
              environment_archive_sha256: string
              error_types: list<item: string>
                child 0, item: string
              first_source_id: string
              grader_sha256: string
              harness_id: string
              harness_version: string
              has_recorded_errors: bool
              identity_status: string
              identity_variant_count: int64
              inference_provenance: struct<cache_precision: null, checkpoint_revision: null, compaction_policy: string, quantization: nu (... 58 chars omitted)
                child 0, cache_precision: null
                child 1, checkpoint_revision: null
                child 2, compaction_policy: string
                child 3, quantization: null
                child 4, serving_implementation: string
                child 5, serving_version: null
              is_canonical_identity_record: bool
              i
              ...
              pu: double
                child 4, creates_per_min: int64
                child 5, disk: double
                child 6, guaranteed: bool
                child 7, id: string
                child 8, idle_timeout: double
                child 9, image: string
                child 10, image_cached: bool
                child 11, labels: list<item: null>
                    child 0, item: null
                child 12, memory: double
                child 13, type: string
                child 14, vm: bool
                child 15, workdir: string
              recorded_status: string
              reference_equivalence: double
              rl_diagnostics_status: string
              run_id: string
              sampling: struct<max_tokens: int64, temperature: double, reasoning_effort: string>
                child 0, max_tokens: int64
                child 1, temperature: double
                child 2, reasoning_effort: string
              source_lines: double
              source_roles: list<item: string>
                child 0, item: string
              started_at_utc: string
              stop_condition: string
              strict_package_reward: double
              task: string
              task_category: string
              task_difficulty: string
              task_wire_sha256: string
              telemetry_schema_version: string
              tests_archive_sha256: string
              trace_id: string
              trace_ok: bool
              trace_schema_version: int64
              trajectory_event_count: int64
              usage_calls_present: int64
              usage_fields_complete: struct<cached_input_tokens: bool, completion_tokens: bool, cost: bool, prompt_tokens: bool, reasonin (... 15 chars omitted)
                child 0, cached_input_tokens: bool
                child 1, completion_tokens: bool
                child 2, cost: bool
                child 3, prompt_tokens: bool
                child 4, reasoning_tokens: bool
              verifiers_version: string
              bytes: int64
              indexed: bool
              mtime: double
              duplicate_name: bool
              archive_source_id: string
              member: string
              to
              {'archive_source_id': Value('string'), 'bytes': Value('int64'), 'duplicate_name': Value('bool'), 'indexed': Value('bool'), 'member': Value('string'), 'mtime': Value('float64')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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archive_source_id
string
bytes
int64
duplicate_name
bool
indexed
bool
member
string
mtime
float64
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,836
false
true
outputs/classical-rl-efficiency--moonshotai--kimi-k3--kimi_code/3ab9d547-4916-4e76-9be9-31d935f4b66b/config.toml
1,788,390,388.651459
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,836
false
true
outputs/classical-rl-efficiency--moonshotai--kimi-k3--kimi_code/51d2c221-bd07-43d6-b92f-7467c77ba3ff/config.toml
1,788,388,249.133579
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,581
false
true
outputs/classical-rl-efficiency--moonshotai--kimi-k3--kimi_code/610b521e-ba52-4d76-a6f5-5502df9dc31a/config.toml
1,788,451,307.003206
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,533
false
true
outputs/classical-rl-efficiency--moonshotai--kimi-k3--kimi_code/85c4c38b-9abc-4624-a85a-0f4af56efe81/config.toml
1,788,469,438.348472
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,626
false
true
outputs/classical-rl-efficiency--moonshotai--kimi-k3--kimi_code/960627a8-e488-4c81-9930-b3cba271794f/config.toml
1,788,455,489.217274
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,811
false
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outputs/classical-rl-efficiency--openai--gpt-5.6-luna--codex/004f6b77-49d6-40a9-9834-619cec65486c/config.toml
1,788,390,388.655481
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,507
false
true
outputs/classical-rl-efficiency--openai--gpt-5.6-luna--codex/1d7c1ea0-1e3b-4696-97b8-74b1c3529ca3/config.toml
1,788,411,609.355118
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,507
false
true
outputs/classical-rl-efficiency--openai--gpt-5.6-luna--codex/ef8650aa-1fba-4204-a74f-6a11500ba3f6/config.toml
1,788,448,856.761269
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,505
false
true
outputs/classical-rl-efficiency--openai--gpt-5.6-sol--codex/6ff0e4b9-f6f1-4d86-8a09-8c82df374036/config.toml
1,788,411,609.355118
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,809
false
true
outputs/classical-rl-efficiency--openai--gpt-5.6-sol--codex/89f54644-757a-4f47-b1c1-5fb393bee028/config.toml
1,788,388,238.610193
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,809
false
true
outputs/classical-rl-efficiency--openai--gpt-5.6-sol--codex/8b4e8a88-2eb2-4b9b-9c46-ea9bf56d09cb/config.toml
1,788,390,388.647438
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,505
false
true
outputs/classical-rl-efficiency--openai--gpt-5.6-sol--codex/a6289940-d1ca-4911-a78a-5aa98d6b47b8/config.toml
1,788,445,859.501392
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,505
false
true
outputs/classical-rl-efficiency--openai--gpt-5.6-sol--codex/c0ababf8-42e5-4f6d-bf4d-2d1d3ae15082/config.toml
1,788,445,247.411029
612a9b3e34bbe74b2e900d4b1212c85b92fbb378f798d1bd35afd1c1a1cb95ea
2,813
false
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outputs/classical-rl-efficiency--openai--gpt-5.6-terra--codex/42f028f2-85aa-45ce-893c-44c452e4474b/config.toml
1,788,390,388.643416
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false
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README.md
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classical_rl_efficiency/__init__.py
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classical_rl_efficiency/analysis.py
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classical_rl_efficiency/behavioral_contract.py
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classical_rl_efficiency/evidence_cli.py
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End of preview.

Classical RL efficiency: privacy-cleaned trace dataset

This is a privacy-redacted derivative of the saved research archive, published on Hugging Face. The private original and the September 23 cleaned snapshot remain unchanged. No inference, grading, rescoring or tokenization was run for this release.

License and publication status

Publicly accessible, not open-licensed. The publisher's original protected material is offered under an All Rights Reserved (Proprietary) notice; see LICENSE. Reuse requires permission except where applicable law, Hugging Face's terms, or an applicable third-party license already permits it. Public visibility allows anyone to view or download the repository; the license is not an access-control mechanism.

Third-party papers, code and other excerpts in traces retain their own terms. The dataset-level notice does not relicense those materials, override their permissions, or resolve the outstanding third-party redistribution review. The privacy verification reports are not comprehensive security or legal clearance. release.json records publication metadata; historical verification/redaction reports retain their original scope and results.

Contents

1,019 original trace identities and 40 labelled derived variants, across 70 recorded run IDs and 2026-08-19 through 2026-09-22 UTC. These are not a single comparable cohort or 1,059 independent experiments. Model/date/protocol inventories and all recorded errors and cap hits are retained. Missing measurements remain missing.

The JSONL trace and linked call/node/tool/edit/telemetry/metric tables are retained, as are CSV indexes and a freshly rebuilt SQLite database. Raw objects, source logs, paper/reference archives and source-restoration tools are deliberately NOT included. Source/archive inventories are provenance metadata only, not downloadable payloads.

Identity and measurement semantics

record_id remains the ORIGINAL content hash for stable joins; it is no longer the hash of the redacted trace. sanitized_trace_sha256 verifies the redacted payload. Episode/source IDs and protocol/config/artifact hashes also refer to ORIGINAL data. Never execute sanitized commands or treat anonymized paths as runnable locators.

All token counts, timing, scores, LOC and other numerical measurements describe the original pre-redaction executions. They are preserved, not recalculated from the edited text. Redacted text will not necessarily retokenize to those counts. Canonical originals and derived variants remain distinct; infrastructure failures are not model-quality zeroes. Compare only compatible frozen protocol/task/config cohorts. Behavioral signatures do not identify undisclosed training coefficients.

Verification and reuse

Recorded validation results are in verification.json. This release contains data and documentation only; maintenance scripts are not included.

This is NOT public-release clearance: third-party licenses, copied paper/code text, benchmark exposure, other identifiers and applicable provider terms still need review. Pattern-based privacy cleaning cannot guarantee exhaustive anonymization. The source archive's credential-like alerts were not confirmed live credentials.
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