Social media leader scales LLM alignment at production

Overview

A foundation model behaves differently when it's the engine behind a global product than when it's running on a benchmark.

When billions of user interactions sit downstream of every weight update, alignment has to hold up across instructions, multi-turn dialog, long-context reasoning, math, and logic, not just one of them.

Firstsource ran the end-to-end alignment program for a Global Social Media client, covering instruction tuning, multi-turn RLHF, long-context evaluation, and advanced reasoning across STEM and logical domains.

The work was Intelligence that Operates: human judgment built into the model lifecycle, not bolted onto its release.

Challenges

• Cross-turn dependency tracking can't be staffed with a general annotation pool. Multi-turn dialog quality depends on context retention across many turns. Standard review treats each turn as isolated and misses the failures that matter.
• Long-context reasoning needs evaluators who can hold the whole document. 8K+ token outputs require senior judgment for reasoning consistency, summarization quality, and factual grounding, not crowd-grade rating.
• STEM and structured logic don't yield to generic annotation. Step-by-step validation of advanced quantitative and domain-specific reasoning problems requires evaluators who can find the wrong step, not raters who only judge the final answer.

How We Made It Happen

We built one program rather than five disconnected efforts, with judgment matched to the work it was evaluating.

• Domain-graded evaluator pools across the alignment stack. Subject-matter experts and adversarial testers were deployed to the workstreams that actually needed them ” STEM, dialog, long-context grounding, and logical inference each routed to credentialed evaluators.
• Expert Preference (RLHF) across the lifecycle, not just at the end. Instruction tuning, dialog preference, long-context evaluation, and reasoning validation ran as a single coordinated program.
• Adversarial pressure built in from the start. Red-team specialists worked alongside SMEs throughout the program, so robustness and quality moved together rather than getting bolted on at the end.

Conclusion

Aligning a foundation model for global consumer use isn't a hand-off between teams ” it's one continuous human signal applied across instruction, dialog, reasoning, and adversarial pressure. Firstsource ran that signal end-to-end, turning alignment into Intelligence that Operates.