t1-facts-strategies-t1-base-eval-hosted_vllm-qwen-qwen3-1-7b-5arg

Synthesized 15 improvement artifacts (rules, algorithms, programs, heuristics, etc.) from traces using gpt-5-mini via RecLM. Source eval accuracy: 0/800 (0.0%).

Dataset Info

• Rows: 15
• Columns: 2

Columns

Column	Type	Description
fact_id	Value('int64')	Sequential fact identifier (0-indexed)
fact	Value('string')	Improvement artifact synthesized from traces (rule, algorithm, program, heuristic, etc.)

Generation Parameters

{
  "script_name": "02d_synthesize_knowledge_strategies.py",
  "model": "gpt-5-mini",
  "hyperparameters": {
    "reclm_backend": "openai",
    "num_facts": 15
  },
  "input_datasets": [
    "t1-base-eval-hosted_vllm-qwen-qwen3-1-7b-5arg"
  ],
  "description": "Synthesized 15 improvement artifacts (rules, algorithms, programs, heuristics, etc.) from traces using gpt-5-mini via RecLM. Source eval accuracy: 0/800 (0.0%).",
  "custom_metadata": {
    "experiment_name": "t1_synthesize_knowledge_improvement",
    "stage": "knowledge_synthesis_improvement_artifacts",
    "ablation_type": "improvement_artifacts_from_traces",
    "data_prompt_length": 1206918,
    "root_prompt_length": 10155,
    "source_eval_total_problems": 100,
    "source_eval_total_samples": 800,
    "source_eval_correct": 0,
    "facts": [
      "Operand inventory rule: At the start, list every provided operand and mark each as unused. Before applying any operation, explicitly select a distinct pair consisting of either two unused operands or one unused operand and one verified intermediate result. After forming an intermediate result, mark the operands that contributed to it as used and record the new intermediate as available. Never proceed if any operand appears twice in the active ledger.",
      "Used-operand ledger algorithm: Maintain a ledger data structure that records for each item whether it is unused, in-progress, or consumed. Procedure: choose a pair from ledger with status unused or available intermediate; compute a new intermediate; update statuses by marking contributors consumed and appending the new intermediate as available. Always persist a snapshot before each update so you can revert cleanly. Use the ledger as the primary authority when validating any candidate final expression.",
      "Backtracking checklist: When a candidate path fails, perform these steps before restarting: revert to the last persisted snapshot, restore ledger statuses, record the failed pairing and operator combination in an attempts log for that structural choice, choose a different operator or a different pairing, and continue. If the same structural choice has already produced repeated failures, abandon that structure and pick a structurally different grouping.",
      "Systematic search strategy: Explore expression structures by increasing structural complexity. For each structure, enumerate distinct operand groupings and operator assignments in a canonical order determined by the ledger. For each candidate, validate with the ledger and compute a score that reflects closeness to the target. Continue search until a validated candidate passes the final verification checklist or until structural alternatives are exhausted, then escalate to wider structure exploration.",
      "Magnitude-aware prioritization heuristic: Estimate whether the target requires amplification or reduction relative to available items. If amplification appears necessary, prefer multiplicative-style combinations early in the construction. If reduction appears necessary, prefer subtractive-style or divisive-style combinations early. Always verify the arithmetic of these high-impact steps immediately and keep alternative pathways recorded.",
      "Final verification checklist: Before outputting an answer, confirm all of the following: the operand ledger shows every original item is present and that no original item is reused; the expression contains no reused contributors; every arithmetic step in the derivation has been recomputed independently; the final computed value was recomputed from the original operands using the recorded sequence of steps; and there is a persisted ledger snapshot that reproduces the derivation. If any item fails, do not finalize.",
      "Resource pool mental model: Think of the provided operands as a finite resource pool. Each operation consumes specific resources and may produce a new resource (an intermediate). Plan by asking how each consumption transforms the pool toward the target. Favor operations that produce intermediates that are easier to combine further rather than intermediates that force many delicate compensations.",
      "Premature-commitment reminder: Do not lock into a single structural decomposition early. After any initial construction, also attempt a materially different structural pathway and compare results after full verification. If the first path passes verification, keep it; otherwise prefer the verified alternative. Track which structural decompositions have been attempted to avoid wasting effort on repeats.",
      "Layered building strategy: Construct the solution by forming explicit intermediate milestones that move the current pool toward the target. For each milestone, require immediate verification and ledger updates. Treat an intermediate as locked only after it has been independently recomputed from its contributors. Use these locked intermediates as stable building blocks for the next layer of combination.",
      "Canonicalization routine: After producing an intermediate, convert it to a canonical representation that normalizes commutative variations and parentheses placement so that equivalent intermediates map to the same canonical token. Use canonical tokens to detect duplicate states, prevent redundant exploration, and record visited states in the attempts log.",
      "State scoring heuristic: For each candidate intermediate or partial construction, compute a qualitative proximity score that ranks how near it is to the target and how many unused resources remain that can plausibly close the gap. Prefer candidates with higher proximity and with flexible remaining resources. Use this ranking to order exploration and to break ties between structurally similar choices.",
      "Validation subroutine: Before accepting any candidate as final, run a validation routine that checks the following: that the ledger uses every original item without reuse; that the derivation steps are internally consistent when replayed from the original items; and that the final computed value matches the recomputed value when replayed from the original items. If any check fails, reject the candidate and record the reason for later analysis.",
      "Operator diversity rule: When multiple consecutive attempts use the same operator pattern or the same dominant structural motif, force variation by selecting a different operator family or by regrouping operands. Maintain an attempts log indexed by structural motif so the search does not get stuck repeating similar failing moves.",
      "Loop-detection procedure: Maintain a visited-state set keyed by the canonicalized ledger state and the list of available intermediates. Before exploring a new partial construction, check if its canonical state has been seen. If so, skip it to avoid repetition. If a path revisits the same canonical state with no new consumed resources, treat it as a loop and abandon that branch.",
      "Final answer framing and accountability template: When presenting a candidate solution, provide the following parts: a concise canonical expression string; a brief replay of the ledger-validated steps used to produce it; and an explicit confirmation that the validation subroutine passed all checks. If any step in the replay cannot be reproduced from the original ledger, do not present the candidate as final and instead return to structured search."
    ],
    "raw_response": "[\n  \"Operand inventory rule: At the start, list every provided operand and mark each as unused. Before applying any operation, explicitly select a distinct pair consisting of either two unused operands or one unused operand and one verified intermediate result. After forming an intermediate result, mark the operands that contributed to it as used and record the new intermediate as available. Never proceed if any operand appears twice in the active ledger.\",\n  \"Used-operand ledger algorithm: Maintain a ledger data structure that records for each item whether it is unused, in-progress, or consumed. Procedure: choose a pair from ledger with status unused or available intermediate; compute a new intermediate; update statuses by marking contributors consumed and appending the new intermediate as available. Always persist a snapshot before each update so you can revert cleanly. Use the ledger as the primary authority when validating any candidate final expression.\",\n  \"Backtracking checklist: When a candidate path fails, perform these steps before restarting: revert to the last persisted snapshot, restore ledger statuses, record the failed pairing and operator combination in an attempts log for that structural choice, choose a different operator or a different pairing, and continue. If the same structural choice has already produced repeated failures, abandon that structure and pick a structurally different grouping.\",\n  \"Systematic search strategy: Explore expression structures by increasing structural complexity. For each structure, enumerate distinct operand groupings and operator assignments in a canonical order determined by the ledger. For each candidate, validate with the ledger and compute a score that reflects closeness to the target. Continue search until a validated candidate passes the final verification checklist or until structural alternatives are exhausted, then escalate to wider structure exploration.\",\n  \"Magnitude-aware prioritization heuristic: Estimate whether the target requires amplification or reduction relative to available items. If amplification appears necessary, prefer multiplicative-style combinations early in the construction. If reduction appears necessary, prefer subtractive-style or divisive-style combinations early. Always verify the arithmetic of these high-impact steps immediately and keep alternative pathways recorded.\",\n  \"Final verification checklist: Before outputting an answer, confirm all of the following: the operand ledger shows every original item is present and that no original item is reused; the expression contains no reused contributors; every arithmetic step in the derivation has been recomputed independently; the final computed value was recomputed from the original operands using the recorded sequence of steps; and there is a persisted ledger snapshot that reproduces the derivation. If any item fails, do not finalize.\",\n  \"Resource pool mental model: Think of the provided operands as a finite resource pool. Each operation consumes specific resources and may produce a new resource (an intermediate). Plan by asking how each consumption transforms the pool toward the target. Favor operations that produce intermediates that are easier to combine further rather than intermediates that force many delicate compensations.\",\n  \"Premature-commitment reminder: Do not lock into a single structural decomposition early. After any initial construction, also attempt a materially different structural pathway and compare results after full verification. If the first path passes verification, keep it; otherwise prefer the verified alternative. Track which structural decompositions have been attempted to avoid wasting effort on repeats.\",\n  \"Layered building strategy: Construct the solution by forming explicit intermediate milestones that move the current pool toward the target. For each milestone, require immediate verification and ledger updates. Treat an intermediate as locked only after it has been independently recomputed from its contributors. Use these locked intermediates as stable building blocks for the next layer of combination.\",\n  \"Canonicalization routine: After producing an intermediate, convert it to a canonical representation that normalizes commutative variations and parentheses placement so that equivalent intermediates map to the same canonical token. Use canonical tokens to detect duplicate states, prevent redundant exploration, and record visited states in the attempts log.\",\n  \"State scoring heuristic: For each candidate intermediate or partial construction, compute a qualitative proximity score that ranks how near it is to the target and how many unused resources remain that can plausibly close the gap. Prefer candidates with higher proximity and with flexible remaining resources. Use this ranking to order exploration and to break ties between structurally similar choices.\",\n  \"Validation subroutine: Before accepting any candidate as final, run a validation routine that checks the following: that the ledger uses every original item without reuse; that the derivation steps are internally consistent when replayed from the original items; and that the final computed value matches the recomputed value when replayed from the original items. If any check fails, reject the candidate and record the reason for later analysis.\",\n  \"Operator diversity rule: When multiple consecutive attempts use the same operator pattern or the same dominant structural motif, force variation by selecting a different operator family or by regrouping operands. Maintain an attempts log indexed by structural motif so the search does not get stuck repeating similar failing moves.\",\n  \"Loop-detection procedure: Maintain a visited-state set keyed by the canonicalized ledger state and the list of available intermediates. Before exploring a new partial construction, check if its canonical state has been seen. If so, skip it to avoid repetition. If a path revisits the same canonical state with no new consumed resources, treat it as a loop and abandon that branch.\",\n  \"Final answer framing and accountability template: When presenting a candidate solution, provide the following parts: a concise canonical expression string; a brief replay of the ledger-validated steps used to produce it; and an explicit confirmation that the validation subroutine passed all checks. If any step in the replay cannot be reproduced from the original ledger, do not present the candidate as final and instead return to structured search.\"\n]"
  }
}

Experiment Documentation

For complete experiment details, see https://github.com/reasoning-degeneration/experiments/t1_synthesize_knowledge_improvement

Usage

from datasets import load_dataset

dataset = load_dataset("reasoning-degeneration-dev/t1-facts-strategies-t1-base-eval-hosted_vllm-qwen-qwen3-1-7b-5arg", split="train")
print(f"Loaded {len(dataset)} rows")

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This dataset is tracked in reasoning-degeneration-dev/PROJECT-MANIFEST