FineEnvs/data-agent-experiment-results

1# Harbor and OpenCode — consolidated training and pass@12 3Updated: 2026-09-16T14:50:03.359104+00:004 5[Live Trackio dashboard](https://huggingface.co/spaces/HuggingEnvs/data-agent-training-comparison-trackio) · [Overview image](comparison.png) · [Snapshot](snapshot.json)6 7Qwen3.5-2B; 1,000 optimizer-step target per run. Recorded training: Harbor multi-harness: 1000 steps; Native OpenCode: 1000 steps; Harbor OpenCode-only: 199 steps. Every accepted checkpoint has 250 fixed tasks × four harnesses = 1,000 grades. Task difficulty: 33 easy, 118 medium, 99 hard (13.2% / 47.2% / 39.6%). Scores retain first graded attempts; incomplete and failed-audit evaluations are excluded. Missing scores are not estimated.8 9Baselines are separate measured cohorts: Harbor/E2B 14.6%; Harbor/Daytona 15.9% for the native OpenCode checkpoint evaluator. The standalone native OpenCode 8.4% baseline uses a different harness protocol and is excluded here. Infrastructure and training recipe histories differ; this is an observational comparison, not a controlled causal experiment.10 11## Overall checkpoint curve12 13| Checkpoint | Harbor multi-harness | Native OpenCode | Harbor OpenCode-only |14| --- | ---: | ---: | ---: |15| 0 (baseline) | 14.6% | 15.9% | 14.6% |16| 100 | 24.8% | 19.7% | Pending |17| 200 | 26.3% | 22.1% | Pending |18| 300 | 28.6% | 21.6% | Pending |19| 400 | 33.3% | 26.4% | Pending |20| 500 | 37.0% | 23.1% | Pending |21| 600 | 31.8% | 25.1% | Pending |22| 684 (recovery) | 32.1% | Not scheduled | Not scheduled |23| 700 | 28.8% | 23.2% | Pending |24| 800 | 27.0% | 25.6% | Pending |25| 900 | Pending | 25.3% | Pending |26| 1000 | Pending | 29.8% | Pending |27 28## Harbor multi-harness29 30### Overall and difficulty31 32| Checkpoint | Overall | Easy (132 cells) | Medium (472) | Hard (396) |33| --- | ---: | ---: | ---: | ---: |34| 0 | 14.6% | 40.2% | 14.4% | 6.3% |35| 100 | 24.8% | 53.0% | 30.5% | 8.6% |36| 200 | 26.3% | 58.3% | 30.1% | 11.1% |37| 300 | 28.6% | 64.4% | 32.8% | 11.6% |38| 400 | 33.3% | 73.5% | 39.6% | 12.4% |39| 500 | 37.0% | 72.7% | 44.3% | 16.4% |40| 600 | 31.8% | 72.7% | 37.3% | 11.6% |41| 684 | 32.1% | 75.8% | 35.8% | 13.1% |42| 700 | 28.8% | 60.6% | 33.9% | 12.1% |43| 800 | 27.0% | 59.1% | 32.6% | 9.6% |44 45### Harness × difficulty at every checkpoint46 47| Checkpoint | Harness | Overall (250) | Easy (33) | Medium (118) | Hard (99) |48| --- | --- | ---: | ---: | ---: | ---: |49| 0 | opencode | 10.8% | 33.3% (11/33) | 8.5% (10/118) | 6.1% (6/99) |50| 0 | claude-code | 16.8% | 42.4% (14/33) | 18.6% (22/118) | 6.1% (6/99) |51| 0 | codex | 16.4% | 42.4% (14/33) | 16.9% (20/118) | 7.1% (7/99) |52| 0 | mini-swe-agent | 14.4% | 42.4% (14/33) | 13.6% (16/118) | 6.1% (6/99) |53| 100 | opencode | 24.4% | 51.5% (17/33) | 31.4% (37/118) | 7.1% (7/99) |54| 100 | claude-code | 27.6% | 60.6% (20/33) | 32.2% (38/118) | 11.1% (11/99) |55| 100 | codex | 28.0% | 57.6% (19/33) | 35.6% (42/118) | 9.1% (9/99) |56| 100 | mini-swe-agent | 19.2% | 42.4% (14/33) | 22.9% (27/118) | 7.1% (7/99) |57| 200 | opencode | 30.4% | 51.5% (17/33) | 34.7% (41/118) | 18.2% (18/99) |58| 200 | claude-code | 30.0% | 63.6% (21/33) | 33.1% (39/118) | 15.2% (15/99) |59| 200 | codex | 26.4% | 63.6% (21/33) | 29.7% (35/118) | 10.1% (10/99) |60| 200 | mini-swe-agent | 18.4% | 54.5% (18/33) | 22.9% (27/118) | 1.0% (1/99) |61| 300 | opencode | 29.6% | 60.6% (20/33) | 33.1% (39/118) | 15.2% (15/99) |62| 300 | claude-code | 33.2% | 66.7% (22/33) | 39.0% (46/118) | 15.2% (15/99) |63| 300 | codex | 29.6% | 69.7% (23/33) | 33.1% (39/118) | 12.1% (12/99) |64| 300 | mini-swe-agent | 22.0% | 60.6% (20/33) | 26.3% (31/118) | 4.0% (4/99) |65| 400 | opencode | 32.8% | 72.7% (24/33) | 38.1% (45/118) | 13.1% (13/99) |66| 400 | claude-code | 36.8% | 75.8% (25/33) | 44.9% (53/118) | 14.1% (14/99) |67| 400 | codex | 34.8% | 72.7% (24/33) | 42.4% (50/118) | 13.1% (13/99) |68| 400 | mini-swe-agent | 28.8% | 72.7% (24/33) | 33.1% (39/118) | 9.1% (9/99) |69| 500 | opencode | 32.8% | 69.7% (23/33) | 40.7% (48/118) | 11.1% (11/99) |70| 500 | claude-code | 44.8% | 75.8% (25/33) | 52.5% (62/118) | 25.3% (25/99) |71| 500 | codex | 39.2% | 75.8% (25/33) | 46.6% (55/118) | 18.2% (18/99) |72| 500 | mini-swe-agent | 31.2% | 69.7% (23/33) | 37.3% (44/118) | 11.1% (11/99) |73| 600 | opencode | 34.0% | 66.7% (22/33) | 41.5% (49/118) | 14.1% (14/99) |74| 600 | claude-code | 30.0% | 72.7% (24/33) | 33.9% (40/118) | 11.1% (11/99) |75| 600 | codex | 32.4% | 66.7% (22/33) | 39.8% (47/118) | 12.1% (12/99) |76| 600 | mini-swe-agent | 30.8% | 84.8% (28/33) | 33.9% (40/118) | 9.1% (9/99) |77| 684 | opencode | 29.2% | 72.7% (24/33) | 30.5% (36/118) | 13.1% (13/99) |78| 684 | claude-code | 35.6% | 72.7% (24/33) | 41.5% (49/118) | 16.2% (16/99) |79| 684 | codex | 33.2% | 84.8% (28/33) | 36.4% (43/118) | 12.1% (12/99) |80| 684 | mini-swe-agent | 30.4% | 72.7% (24/33) | 34.7% (41/118) | 11.1% (11/99) |81| 700 | opencode | 21.2% | 30.3% (10/33) | 28.0% (33/118) | 10.1% (10/99) |82| 700 | claude-code | 31.6% | 78.8% (26/33) | 34.7% (41/118) | 12.1% (12/99) |83| 700 | codex | 32.0% | 72.7% (24/33) | 34.7% (41/118) | 15.2% (15/99) |84| 700 | mini-swe-agent | 30.4% | 60.6% (20/33) | 38.1% (45/118) | 11.1% (11/99) |85| 800 | opencode | 23.2% | 36.4% (12/33) | 33.1% (39/118) | 7.1% (7/99) |86| 800 | claude-code | 32.0% | 66.7% (22/33) | 37.3% (44/118) | 14.1% (14/99) |87| 800 | codex | 26.8% | 66.7% (22/33) | 31.4% (37/118) | 8.1% (8/99) |88| 800 | mini-swe-agent | 26.0% | 66.7% (22/33) | 28.8% (34/118) | 9.1% (9/99) |89 90### Training history91 92| Allocation | First optimizer step | Last optimizer step |93| --- | ---: | ---: |94| 78647 | 1 | 17 |95| 78681 | 18 | 25 |96| 78767 | 26 | 30 |97| 78831 | 31 | 53 |98| 78956 | 54 | 196 |99| 79083 | 197 | 684 |100| 80608 | 685 | 1000 |101 102### Score provenance103 104- Step 0: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-baseline-20260914/job-78215/canonical_results.json`; SHA256 `8c4f5bced4eff04b0c2e5f41806da9ae1b8a4c0fe356ddf926e1f781c6bb9ac6`.105- Step 100: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-bounded-20260915/checkpoint-evals/step-000100/scores.json`; SHA256 `1e4f42f54526c9b8b71156f5b1f3673529519201401bc88f88ccff805f291181`.106- Step 200: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-prod-20260915/checkpoint-evals/step-000200/scores.json`; SHA256 `699dce549d1002e879c675555ac06142448b0cbeef534a0816616faf669862af`.107- Step 300: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-prod-20260915/checkpoint-evals/step-000300/scores.json`; SHA256 `299e5ed3528922d9912a591f3cff0c4d85070fef4de732d37723967934bfd691`.108- Step 400: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-prod-20260915/checkpoint-evals/step-000400/scores.json`; SHA256 `f8909af81669f4ec092317c5f9889ef8735754c0dcbc826da82a270d2d92dc7a`.109- Step 500: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-prod-20260915/checkpoint-evals/step-000500/scores.json`; SHA256 `86d56b65edbdc2f5a3f5d54151d0888dae83ca2b81753a7a9dbc2e80ee4f6130`.110- Step 600: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-prod-20260915/checkpoint-evals/step-000600/scores.json`; SHA256 `02fc5a5e84978c198dc880c143fe223507c30485563b3545cb64b2794e3e60b0`.111- Step 684: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-prod-20260915/checkpoint-evals/step-000684/scores.json`; SHA256 `fb54bd52f352463e405bee8067ef24b27147bfd02d41429561f440a916f5d776`.112- Step 700: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-prod-cont-20260915/checkpoint-evals/step-000700/scores.json`; SHA256 `39c2ead8681847c6c398eb6b22ae8919aabaff3ad6b668d81b5961564139f8be`.113- Step 800: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-long-prod-cont-20260915/checkpoint-evals/step-000800/scores.json`; SHA256 `65130786707d07f68cfa145fcd5ad7308890cb89a305b65382dfbec36ef7a150`.114 115## Native OpenCode116 117### Overall and difficulty118 119| Checkpoint | Overall | Easy (132 cells) | Medium (472) | Hard (396) |120| --- | ---: | ---: | ---: | ---: |121| 0 | 15.9% | 37.9% | 18.0% | 6.1% |122| 100 | 19.7% | 44.7% | 22.5% | 8.1% |123| 200 | 22.1% | 59.8% | 24.6% | 6.6% |124| 300 | 21.6% | 51.5% | 25.6% | 6.8% |125| 400 | 26.4% | 59.1% | 29.4% | 11.9% |126| 500 | 23.1% | 53.0% | 27.3% | 8.1% |127| 600 | 25.1% | 56.1% | 28.8% | 10.4% |128| 700 | 23.2% | 49.2% | 28.8% | 7.8% |129| 800 | 25.6% | 52.3% | 29.7% | 11.9% |130| 900 | 25.3% | 50.0% | 30.3% | 11.1% |131| 1000 | 29.8% | 59.8% | 35.8% | 12.6% |132 133### Harness × difficulty at every checkpoint134 135| Checkpoint | Harness | Overall (250) | Easy (33) | Medium (118) | Hard (99) |136| --- | --- | ---: | ---: | ---: | ---: |137| 0 | opencode | 12.8% | 24.2% (8/33) | 16.9% (20/118) | 4.0% (4/99) |138| 0 | claude-code | 16.8% | 42.4% (14/33) | 18.6% (22/118) | 6.1% (6/99) |139| 0 | codex | 15.2% | 33.3% (11/33) | 16.9% (20/118) | 7.1% (7/99) |140| 0 | mini-swe-agent | 18.8% | 51.5% (17/33) | 19.5% (23/118) | 7.1% (7/99) |141| 100 | opencode | 19.6% | 42.4% (14/33) | 24.6% (29/118) | 6.1% (6/99) |142| 100 | claude-code | 20.4% | 42.4% (14/33) | 22.9% (27/118) | 10.1% (10/99) |143| 100 | codex | 18.0% | 27.3% (9/33) | 21.2% (25/118) | 11.1% (11/99) |144| 100 | mini-swe-agent | 20.8% | 66.7% (22/33) | 21.2% (25/118) | 5.1% (5/99) |145| 200 | opencode | 17.2% | 48.5% (16/33) | 21.2% (25/118) | 2.0% (2/99) |146| 200 | claude-code | 24.0% | 60.6% (20/33) | 26.3% (31/118) | 9.1% (9/99) |147| 200 | codex | 25.6% | 63.6% (21/33) | 28.8% (34/118) | 9.1% (9/99) |148| 200 | mini-swe-agent | 21.6% | 66.7% (22/33) | 22.0% (26/118) | 6.1% (6/99) |149| 300 | opencode | 20.8% | 48.5% (16/33) | 28.0% (33/118) | 3.0% (3/99) |150| 300 | claude-code | 29.2% | 66.7% (22/33) | 30.5% (36/118) | 15.2% (15/99) |151| 300 | codex | 16.8% | 39.4% (13/33) | 20.3% (24/118) | 5.1% (5/99) |152| 300 | mini-swe-agent | 19.6% | 51.5% (17/33) | 23.7% (28/118) | 4.0% (4/99) |153| 400 | opencode | 20.4% | 48.5% (16/33) | 24.6% (29/118) | 6.1% (6/99) |154| 400 | claude-code | 32.4% | 60.6% (20/33) | 35.6% (42/118) | 19.2% (19/99) |155| 400 | codex | 25.6% | 57.6% (19/33) | 28.0% (33/118) | 12.1% (12/99) |156| 400 | mini-swe-agent | 27.2% | 69.7% (23/33) | 29.7% (35/118) | 10.1% (10/99) |157| 500 | opencode | 18.0% | 48.5% (16/33) | 19.5% (23/118) | 6.1% (6/99) |158| 500 | claude-code | 26.4% | 51.5% (17/33) | 33.1% (39/118) | 10.1% (10/99) |159| 500 | codex | 18.8% | 48.5% (16/33) | 22.0% (26/118) | 5.1% (5/99) |160| 500 | mini-swe-agent | 29.2% | 63.6% (21/33) | 34.7% (41/118) | 11.1% (11/99) |161| 600 | opencode | 16.8% | 42.4% (14/33) | 18.6% (22/118) | 6.1% (6/99) |162| 600 | claude-code | 32.4% | 63.6% (21/33) | 37.3% (44/118) | 16.2% (16/99) |163| 600 | codex | 18.4% | 45.5% (15/33) | 23.7% (28/118) | 3.0% (3/99) |164| 600 | mini-swe-agent | 32.8% | 72.7% (24/33) | 35.6% (42/118) | 16.2% (16/99) |165| 700 | opencode | 18.4% | 42.4% (14/33) | 23.7% (28/118) | 4.0% (4/99) |166| 700 | claude-code | 29.6% | 63.6% (21/33) | 33.1% (39/118) | 14.1% (14/99) |167| 700 | codex | 14.4% | 24.2% (8/33) | 22.9% (27/118) | 1.0% (1/99) |168| 700 | mini-swe-agent | 30.4% | 66.7% (22/33) | 35.6% (42/118) | 12.1% (12/99) |169| 800 | opencode | 20.0% | 36.4% (12/33) | 26.3% (31/118) | 7.1% (7/99) |170| 800 | claude-code | 32.4% | 72.7% (24/33) | 35.6% (42/118) | 15.2% (15/99) |171| 800 | codex | 18.0% | 30.3% (10/33) | 21.2% (25/118) | 10.1% (10/99) |172| 800 | mini-swe-agent | 32.0% | 69.7% (23/33) | 35.6% (42/118) | 15.2% (15/99) |173| 900 | opencode | 18.8% | 33.3% (11/33) | 23.7% (28/118) | 8.1% (8/99) |174| 900 | claude-code | 34.0% | 60.6% (20/33) | 42.4% (50/118) | 15.2% (15/99) |175| 900 | codex | 14.4% | 27.3% (9/33) | 17.8% (21/118) | 6.1% (6/99) |176| 900 | mini-swe-agent | 34.0% | 78.8% (26/33) | 37.3% (44/118) | 15.2% (15/99) |177| 1000 | opencode | 20.4% | 42.4% (14/33) | 25.4% (30/118) | 7.1% (7/99) |178| 1000 | claude-code | 33.2% | 66.7% (22/33) | 37.3% (44/118) | 17.2% (17/99) |179| 1000 | codex | 29.6% | 57.6% (19/33) | 35.6% (42/118) | 13.1% (13/99) |180| 1000 | mini-swe-agent | 36.0% | 72.7% (24/33) | 44.9% (53/118) | 13.1% (13/99) |181 182### Training history183 184| Allocation | First optimizer step | Last optimizer step |185| --- | ---: | ---: |186| 80626 | 1 | 1000 |187 188### Score provenance189 190- Step 0: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/20260915/blackbox/canonical_scores.json`; SHA256 `ece2b0e03c7c7e0e54988eaaa473ba6b53bd6028315b1e99ec39df5137c7632e`.191- Step 100: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-80657/canonical_scores.json`; SHA256 `37eab8fe9e97fda23e9803846f965b08c3de2a6e4142363219a4467af41c6f5c`.192- Step 200: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-80675/canonical_scores.json`; SHA256 `fa67547d19c7f4e63166fc7c1518ca15eb5e2c28ec8f1f3739445029006617ad`.193- Step 300: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-80748/canonical_scores.json`; SHA256 `a0e0cc489e3195827d5ac035945277bfd9ca67e985a015148e5b3c94ca938b0f`.194- Step 400: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-80807/canonical_scores.json`; SHA256 `f30bb541e207a2e8b83b2aabd05bf2e3d96eeac40c2fd8226ff1985606397a7b`.195- Step 500: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-80861/canonical_scores.json`; SHA256 `b6df2570695c2a15ba43f185719b647dc22319eb82ca1494d56e705572e3f1a2`.196- Step 600: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-80902/canonical_scores.json`; SHA256 `d86128c2cae4813a7ae8b99f06d1cd8ca109cefade9944c1cdbebf2b1550e1d9`.197- Step 700: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-80956/canonical_scores.json`; SHA256 `7c60cfab333948e63bf44bd01ed4ce3f0a788f76de84c4ceda2177144e9bc9ba`.198- Step 800: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-80993/canonical_scores.json`; SHA256 `b689c9dd76c3e2230bfea49eea393f2d5842fe8f3630f04f59380a131497ae98`.199- Step 900: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-81034/canonical_scores.json`; SHA256 `996f5ea262419b9639fa8f33c1b33fef9b49959c1cbe61e62ba922c0d642985f`.200- Step 1000: `/fsx/adithyaskolavi/projects/trl_prod/experiments/daytona_harness_comparison/logs/hf-20260915/local-opencode-smoke-v4/repro/outputs/local-eval-opencode-81098/canonical_scores.json`; SHA256 `1355a9a2ecc1ec165cf413120dacfc672e5d8d59ef2807b28bcf02322dca142b`.201 202## Harbor OpenCode-only203 204### Overall and difficulty205 206| Checkpoint | Overall | Easy (132 cells) | Medium (472) | Hard (396) |207| --- | ---: | ---: | ---: | ---: |208| 0 | 14.6% | 40.2% | 14.4% | 6.3% |209 210### Harness × difficulty at every checkpoint211 212| Checkpoint | Harness | Overall (250) | Easy (33) | Medium (118) | Hard (99) |213| --- | --- | ---: | ---: | ---: | ---: |214| 0 | opencode | 10.8% | 33.3% (11/33) | 8.5% (10/118) | 6.1% (6/99) |215| 0 | claude-code | 16.8% | 42.4% (14/33) | 18.6% (22/118) | 6.1% (6/99) |216| 0 | codex | 16.4% | 42.4% (14/33) | 16.9% (20/118) | 7.1% (7/99) |217| 0 | mini-swe-agent | 14.4% | 42.4% (14/33) | 13.6% (16/118) | 6.1% (6/99) |218 219### Training history220 221| Allocation | First optimizer step | Last optimizer step |222| --- | ---: | ---: |223| 81075 | 1 | 199 |224 225### Score provenance226 227- Step 0: `/fsx/adithyaskolavi/projects/trl_prod/experiments/async_grpo_harbor_data_agent/logs/multi4-baseline-20260914/job-78215/canonical_results.json`; SHA256 `8c4f5bced4eff04b0c2e5f41806da9ae1b8a4c0fe356ddf926e1f781c6bb9ac6`.228 229## Dashboard metric guide230 231Both runs use identical metric names and optimizer-step axes. `eval/pass_at_1` is the overall score; `eval/difficulty/*` aggregates each difficulty; `eval/harness/*` compares each harness; `eval/harness_difficulty/*` contains all twelve intersections. `train/*` preserves recorded loss, reward, learning rate, gradient norm, entropy, KL, staleness, throughput, token, batching and rollout metrics where observed. Missing metrics are not filled with zeros. `train/reward_rolling20` and `train/nonzero_gradient_rolling20` are explicitly derived trailing windows. Raw metrics remain available. Use zero dashboard smoothing for exact checkpoint values.232 233The independent CPU publisher refreshes every 60 seconds and admits new evaluations only after their full comparison gates pass. It never changes trainer state. Local SQLite backup, event ledger and remote exact-content verification receipts are kept alongside this report.234 235Storage and deployment follow the [Trackio guide](https://huggingface.co/docs/trackio/quickstart) and [environment configuration](https://huggingface.co/docs/trackio/environment_variables).236 237- [Overview](https://huggingenvs-data-agent-training-comparison-trackio.hf.space/?project=qwen35-2b-harbor-vs-opencode-20260916&run_ids=3ae29a23763093285702b71a1f76805a%2Cbeb2604c8f737da4262b1ab19b8b0cbd%2Cc5b445fa337b56b139c1e6b34ae35409&smoothing=0&metric_filter=%5E%28eval%2Fpass_at_1%7Ctrain%2Freward_rolling20%29%24)238- [Difficulty](https://huggingenvs-data-agent-training-comparison-trackio.hf.space/?project=qwen35-2b-harbor-vs-opencode-20260916&run_ids=3ae29a23763093285702b71a1f76805a%2Cbeb2604c8f737da4262b1ab19b8b0cbd%2Cc5b445fa337b56b139c1e6b34ae35409&smoothing=0&metric_filter=%5Eeval%2Fdifficulty%2F)239- [Harness](https://huggingenvs-data-agent-training-comparison-trackio.hf.space/?project=qwen35-2b-harbor-vs-opencode-20260916&run_ids=3ae29a23763093285702b71a1f76805a%2Cbeb2604c8f737da4262b1ab19b8b0cbd%2Cc5b445fa337b56b139c1e6b34ae35409&smoothing=0&metric_filter=%5Eeval%2Fharness%2F)240- [Harness × difficulty](https://huggingenvs-data-agent-training-comparison-trackio.hf.space/?project=qwen35-2b-harbor-vs-opencode-20260916&run_ids=3ae29a23763093285702b71a1f76805a%2Cbeb2604c8f737da4262b1ab19b8b0cbd%2Cc5b445fa337b56b139c1e6b34ae35409&smoothing=0&metric_filter=%5Eeval%2Fharness_difficulty%2F)241- [Optimizer diagnostics](https://huggingenvs-data-agent-training-comparison-trackio.hf.space/?project=qwen35-2b-harbor-vs-opencode-20260916&run_ids=3ae29a23763093285702b71a1f76805a%2Cbeb2604c8f737da4262b1ab19b8b0cbd%2Cc5b445fa337b56b139c1e6b34ae35409&smoothing=0&metric_filter=%5Etrain%2F%28loss%7Cgrad_norm%7Centropy%7Ckl%7Clearning_rate%7Cnonzero_gradient_rolling20%29%24)242- [Throughput and rollout diagnostics](https://huggingenvs-data-agent-training-comparison-trackio.hf.space/?project=qwen35-2b-harbor-vs-opencode-20260916&run_ids=3ae29a23763093285702b71a1f76805a%2Cbeb2604c8f737da4262b1ab19b8b0cbd%2Cc5b445fa337b56b139c1e6b34ae35409&smoothing=0&metric_filter=%5Etrain%2F%28perf%7Crollout%7Csample%7Cbatch%29%2F)243- [All metrics](https://huggingenvs-data-agent-training-comparison-trackio.hf.space/?project=qwen35-2b-harbor-vs-opencode-20260916&run_ids=3ae29a23763093285702b71a1f76805a%2Cbeb2604c8f737da4262b1ab19b8b0cbd%2Cc5b445fa337b56b139c1e6b34ae35409&smoothing=0&metric_filter=)244 245[Download checkpoint scores as CSV](checkpoint_scores.csv)246