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| if not isinstance(action, CodeAction): | ||
| raise TypeError(f"Expected CodeAction, got {type(action)!r}") | ||
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| # TODO: replace dummy response with the call to the code executor inside the container |
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@Darktex - I ll iterate on this part. But, take a look at overall layout.
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Fix: Create robust client parser for server responses
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Nov 19, 2025
Add boiler plate code for CodingEnv
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Fix: Create robust client parser for server responses
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This was referenced Jan 13, 2026
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Formal Round 2 submission document targeting Theme huggingface#3.1 Professional Tasks / World Modeling, with Scaler AI Labs Multi-App Enterprise Workflow sub-theme as the bonus-prize target. Structure (2126 words, 31 section markers, optimized for scannability at 800-project judging scale): * At-a-glance hero-metrics table up top (10 rows with file/line evidence links) — 30-second first-cut scan payload * 30-second hook paragraph — enterprise-workflow framing first, Indian GST as instantiation second, 14M-business scale hook * Six required sections per Meta's brief: problem, environment, agent capabilities, tasks, reward model & evaluation logic, post-training / self-improvement strategy * Post-training section gets the longest treatment (differentiator) — empirical triple-failure-mode matrix from pre-onsite training attempts motivates the two-phase SFT warm-start + GRPO polish plan * OpenEnv alignment checklist — explicitly hits every minimum requirement * On-site compute plan — hour-by-hour breakdown (0-2 trajectory gen, 2-6 SFT, 6-10 GRPO polish, 10-12 docs + video) * "What distinguishes this from 800-project field" — 6 bullets directly mapping to judge psychology signals (rigor, novelty, evidence, plan, min-reqs) Citation-backed throughout: every major claim cites a file path or line number in the repo for judges doing deep review. Not changed: rewards.py, tests, any code; this is pure documentation. Rollback: git revert HEAD. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Fresh Claude reading the briefing cold now has explicit knowledge of: - Round 2 Theme huggingface#3.1 + Scaler AI Labs sub-theme positioning - Theme-criterion-to-artifact map (7-row table) so "which theme?" has a 30-second answer - Round 1 rubric weights (40/30/20/10) with weak-cell flag on cell 3 (Showing Improvement in Rewards) and the explicit directive to prioritize training over doc polish on-site - Minimum-requirement checklist (4 items) to verify before pitch - Unsloth honesty note: docs say drop-in ready, scripts do not wire it - Known-stale-numbers list: EXEC_SUMMARY.md line 8 says "41 tests", real count is 42; fix silently during Phase 4 edits Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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