Lila·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Technical phone screen for an ML Engineer role at Lila, pretty deep on LLM post-training specifically RLVR. One big question that kept branching into sub-topics for a solid chunk of the call. Not a bad experience but it was dense.

Questions Asked (1)

Walk me through RLVR as it's used in modern LLM post-training. What makes a reward 'verifiable', how does it compare to preference-based approaches, what optimizers are commonly used, how do you set up rollouts, what failure modes should you worry about, and when would you pick RLVR over SFT or DPO?

Technical Trade-offsSystem DesignAlgorithms & Data Structures

Author's notes

This was basically six questions duct-taped together and I didn't realize that until I was already three minutes into explaining verifiable vs learned rewards.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by defining RLVR and its role in post-training, then systematically address each sub-question: verifiability, comparison to preference-based methods, optimizers, rollout setup, failure modes, and selection criteria. Use concrete examples and trade-offs to demonstrate depth, and conclude with a decision framework for when to use RLVR.

Pro tip: Emphasize that verifiable rewards are not just about correctness but also about robustness to reward hacking; mention techniques like reward model ensembles or programmatic checks to mitigate this. Also, highlight that RLVR is most effective when the base model already has strong reasoning capabilities, as it refines rather than teaches new skills.

1. Define RLVR and Verifiable Rewards

Explain that RLVR (Reinforcement Learning with Verifiable Rewards) uses automatically checkable reward signals, such as unit tests, math solvers, or formal verifiers, to provide precise feedback. Contrast with learned reward models that approximate human preferences.

2. Compare to Preference-Based Approaches

Discuss how RLVR differs from RLHF/DPO: RLVR relies on ground-truth verification, reducing reward hacking but limiting applicability to domains with verifiable outcomes. Preference-based methods are more general but noisier and require human labels.

3. Detail Optimizers and Rollout Setup

Mention common optimizers like PPO, GRPO, or REINFORCE with baselines, and how rollouts are generated: sample multiple completions per prompt, compute verifiable rewards, and update the policy. Discuss infrastructure needs like parallel sampling and reward computation.

4. Identify Failure Modes

Cover failure modes: reward hacking (e.g., exploiting verifier bugs), distribution shift, sparse rewards, and high variance. Suggest mitigations like reward shaping, curriculum learning, and robust verifiers.

5. Decide When to Use RLVR vs SFT/DPO

Provide criteria: use RLVR when tasks have clear, automatic verification (e.g., code, math) and you need to improve reasoning; use SFT for teaching new formats or knowledge; use DPO for aligning to subjective preferences with limited data.

Key Points to Mention

Definition of verifiable rewards: programmatic checks (unit tests, math solvers, formal proofs) that provide binary or scalar feedback without human judgment.
Comparison to RLHF/DPO: RLVR avoids reward model bias and hacking but requires domain-specific verifiers; DPO is simpler but less precise for reasoning tasks.
Common optimizers: PPO with KL penalty, GRPO (Group Relative Policy Optimization), and REINFORCE with baselines; importance of advantage estimation.
Rollout setup: generating multiple samples per prompt, batching, using temperature sampling, and parallelizing reward computation.
Failure modes: reward hacking, sparse rewards, high variance, and overfitting to verifier; mitigation via reward shaping, ensembles, and held-out verification.
Selection criteria: RLVR for tasks with objective correctness (code, math), SFT for new capabilities, DPO for subjective alignment with limited preference data.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.