add trajectory JSONL streaming for persistence/debug/replay

[thanay-sisir]

Feature: Crash-Safe Trajectory Streaming

1. Why This Matters

This addresses a critical data loss risk during evaluations.

• The Problem: Previously, trajectory data (observations, actions, state) was stored entirely in RAM until the task finished.
• The Risk: If the run crashed 90% of the way through (due to an API error, Out Of Memory, or timeout), all data was lost. You couldn't see why it failed because the logs died with the process.
• The Fix: We now stream data to a file on disk (.jsonl) in real-time. Every single step is written and saved immediately.

2. Impact on Codebase

• File Modified: run.py
• The Mechanism: Added logic to open a file (result_dir/{task_id}.traj.jsonl) at the start of a task.
• The Flow: After every trajectory.append(), the system immediately writes the new item to the file and flushes the buffer.
• Result: Even if the power goes out at Step 29 of 30, you still have a valid file containing steps 1-29 on your disk.
• Performance: Zero noticeable overhead (file writes are negligible compared to LLM inference time).

3. Consequences of Ignoring It

• Lost Hours: A crash in a long-running task means hours of compute time wasted with zero artifacts to show for it.
• Memory Bloat: storing massive HTML observations in RAM causes OOM (Out Of Memory) crashes on long tasks.
• Blind Debugging: Without a partial trajectory file, debugging a crash requires re-running the entire task and hoping it crashes the same way again.

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