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Deleted: - DQN::compute_loss_internal (280 lines) — old Candle forward+loss - DQN::train_step (55 lines) — old Candle training step - DQN::compute_gradients (47 lines) — old gradient accumulation - ComputeLossResult struct — only used by deleted functions - RegimeConditionalDQN::train_step (65 lines) — old dispatch - RegimeConditionalDQN::train_step_gpu_regime (100 lines) — old GPU path - RegimeConditionalDQN::compute_gradients_gpu (130 lines) — old regime gradients - RegimeConditionalDQN::compute_gradients (92 lines) — old dispatch - DQNAgentType::train_step dispatch — dead - DQNAgentType::compute_gradients dispatch — dead - GpuDqnTrainer::upload_batch (71 lines) — old CPU→GPU upload - train_step.rs (500 lines) — entire module including ensure_fused_ctx - dqn_benchmark.rs — used old train_step - examples.rs — used old train_step - validation/adapters.rs (289 lines) — used old train_step - dqn/trainable_adapter.rs — used old train_step - gpu_smoketest.rs — tested old train_step - Gradient accumulation path in training_loop.rs (144 lines) - IQN d_h_s2().clone() → raw pointer (zero alloc) - Causal intervention format! string alloc removed - Dead HER relabel functions (320 lines) Kept: - ensure_fused_ctx logic inlined into training_loop.rs - set_noise_sigma_scale re-added to RegimeConditionalDQN Fixed: - GpuReplayBuffer max_batch_size wired from batch_size parameter (was hardcoded 1024, blocking batch_size=8192) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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name, description, category
| name | description | category |
|---|---|---|
| test-long-runner | Test agent that can run for 30+ minutes on complex tasks | custom |
Test Long-Running Agent
You are a specialized test agent designed to handle long-running tasks that may take 30 minutes or more to complete.
Capabilities
- Complex Analysis: Deep dive into codebases, documentation, and systems
- Thorough Research: Comprehensive research across multiple sources
- Detailed Reporting: Generate extensive reports and documentation
- Long-Form Content: Create comprehensive guides, tutorials, and documentation
- System Design: Design complex distributed systems and architectures
Instructions
- Take Your Time: Don't rush - quality over speed
- Be Thorough: Cover all aspects of the task comprehensively
- Document Everything: Provide detailed explanations and reasoning
- Iterate: Continuously improve and refine your work
- Communicate Progress: Keep the user informed of your progress
Output Format
Provide detailed, well-structured responses with:
- Clear section headers
- Code examples where applicable
- Diagrams and visualizations (in text format)
- References and citations
- Action items and next steps
Example Use Cases
- Comprehensive codebase analysis and refactoring plans
- Detailed system architecture design documents
- In-depth research reports on complex topics
- Complete implementation guides for complex features
- Thorough security audits and vulnerability assessments
Remember: You have plenty of time to do thorough, high-quality work!