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README.md CHANGED
@@ -41,27 +41,27 @@ Several subsets under one dataset path. Pick the one that fits your use-case; re
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  | Config | Rows | Schema | Best for |
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  |---|---:|---|---|
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- | `client_v1_full_corpus_raw` (default) | **23527** | full interaction (prompt + rawResponse + decision blob + call metadata) | failure-mode research, replay, end-to-end audit |
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- | `client_v1_teacher_clean_raw` | **9937** | full interaction | fine-tuning, honest training-quality subset (single teacher model, current schema, non-stalled) |
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- | `client_v1_teacher_clean_lean` | **9937** | derived per-decision (flat schema; see *Fields*) | quick analytics, lightweight loading, headline-statistics work |
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- | `client_v1_26b_raw` | **1843** | full interaction | comparison: the `gemma-4-26b-a4b-it` MoE cohort alone (current schema, stalled decisions kept, 1 winning session) |
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- | `client_v1_26b_lean` | **1843** | derived per-decision (flat schema) | comparison analytics: the MoE on matched game states |
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  ```python
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  from datasets import load_dataset
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  # Default -- the full corpus, including failure modes
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- full = load_dataset("chayuto/klondike-llm-decisions") # 23527 rows
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  # The training-friendly subset (filtered, single teacher)
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- clean_raw = load_dataset("chayuto/klondike-llm-decisions", "client_v1_teacher_clean_raw") # 9937 rows
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- clean_lean = load_dataset("chayuto/klondike-llm-decisions", "client_v1_teacher_clean_lean") # 9937 rows, flat schema
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  # The 26B MoE cohort on its own, for comparison
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- moe_26b = load_dataset("chayuto/klondike-llm-decisions", "client_v1_26b_raw") # 1843 rows
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  ```
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- The `client_v1_26b_*` configs are a **behavioural-comparison cohort**: all 1843 current-schema decisions from the `gemma-4-26b-a4b-it` MoE, with stalled/loop decisions deliberately kept (1115 of 1843 sit inside a stall, where the `teacher_clean` configs drop such rows). The corpus now holds 1 winning session for the 26B MoE; the cohort still centres on failure-mode contrast (stalled/loop decisions kept), so treat it as a behavioural contrast set more than as additional training data.
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  ### Filtering by model
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@@ -79,10 +79,10 @@ The `client_v1_teacher_clean_*` configs are already filtered to a single teacher
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  Collected via an external client-side harness (closed-source) running the Klondike app and capturing every teacher-advisor call. Each game seeds a reproducible deal. Rows are deduplicated by their UUIDv7 `id` across re-exports; nothing is discarded.
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- - **Collection window**: 2026-05-17 to 2026-06-19
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- - **Sessions**: 134 distinct game sessions
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- - **Models**: `gemma-4-31b-it` (19331), `gemma-4-26b-a4b-it` (1843), `gemini-3.1-flash-lite` (1661), `MiniMax-M3` (692)
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- - **Schema tiers**: current (23156), legacy (371)
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  ### Planned: `server_v1_*` configs
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@@ -117,12 +117,12 @@ Derived per-decision rows, flattened. Built by joining each successful interacti
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  | Move type | Count | Share |
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  |---|---:|---:|
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- | `draw_card` | 11673 | 50% |
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- | `tableau_to_tableau` | 6685 | 28% |
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- | `tableau_to_foundation` | 2060 | 9% |
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- | `discard_to_tableau` | 1225 | 5% |
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- | `discard_to_foundation` | 949 | 4% |
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- | `recycle_stock` | 935 | 4% |
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  ## Failure modes are a feature of `*_full_corpus_raw`, not a bug
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  | Config | Rows | Schema | Best for |
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  |---|---:|---|---|
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+ | `client_v1_full_corpus_raw` (default) | **28904** | full interaction (prompt + rawResponse + decision blob + call metadata) | failure-mode research, replay, end-to-end audit |
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+ | `client_v1_teacher_clean_raw` | **12021** | full interaction | fine-tuning, honest training-quality subset (single teacher model, current schema, non-stalled) |
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+ | `client_v1_teacher_clean_lean` | **12021** | derived per-decision (flat schema; see *Fields*) | quick analytics, lightweight loading, headline-statistics work |
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+ | `client_v1_26b_raw` | **1949** | full interaction | comparison: the `gemma-4-26b-a4b-it` MoE cohort alone (current schema, stalled decisions kept, 1 winning session) |
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+ | `client_v1_26b_lean` | **1949** | derived per-decision (flat schema) | comparison analytics: the MoE on matched game states |
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  ```python
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  from datasets import load_dataset
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  # Default -- the full corpus, including failure modes
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+ full = load_dataset("chayuto/klondike-llm-decisions") # 28904 rows
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  # The training-friendly subset (filtered, single teacher)
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+ clean_raw = load_dataset("chayuto/klondike-llm-decisions", "client_v1_teacher_clean_raw") # 12021 rows
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+ clean_lean = load_dataset("chayuto/klondike-llm-decisions", "client_v1_teacher_clean_lean") # 12021 rows, flat schema
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  # The 26B MoE cohort on its own, for comparison
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+ moe_26b = load_dataset("chayuto/klondike-llm-decisions", "client_v1_26b_raw") # 1949 rows
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  ```
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+ The `client_v1_26b_*` configs are a **behavioural-comparison cohort**: all 1949 current-schema decisions from the `gemma-4-26b-a4b-it` MoE, with stalled/loop decisions deliberately kept (1221 of 1949 sit inside a stall, where the `teacher_clean` configs drop such rows). The corpus now holds 1 winning session for the 26B MoE; the cohort still centres on failure-mode contrast (stalled/loop decisions kept), so treat it as a behavioural contrast set more than as additional training data.
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  ### Filtering by model
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  Collected via an external client-side harness (closed-source) running the Klondike app and capturing every teacher-advisor call. Each game seeds a reproducible deal. Rows are deduplicated by their UUIDv7 `id` across re-exports; nothing is discarded.
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+ - **Collection window**: 2026-05-17 to 2026-06-26
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+ - **Sessions**: 161 distinct game sessions
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+ - **Models**: `gemma-4-31b-it` (23235), `gemini-3.1-flash-lite` (2692), `gemma-4-26b-a4b-it` (1949), `MiniMax-M3` (1028)
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+ - **Schema tiers**: current (28533), legacy (371)
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  ### Planned: `server_v1_*` configs
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  | Move type | Count | Share |
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  |---|---:|---:|
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+ | `draw_card` | 13545 | 47% |
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+ | `tableau_to_tableau` | 8874 | 31% |
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+ | `tableau_to_foundation` | 2642 | 9% |
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+ | `discard_to_tableau` | 1553 | 5% |
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+ | `discard_to_foundation` | 1159 | 4% |
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+ | `recycle_stock` | 1131 | 4% |
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  ## Failure modes are a feature of `*_full_corpus_raw`, not a bug
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