Search papers, labs, and topics across Lattice.
22 papers published across 5 labs.
Aligning video generation models to human intent and physical constraints is significantly harder than for text or images, necessitating a diverse suite of post-training techniques beyond simple fine-tuning.
A single capable verifier can dramatically improve terminal agent action reliability, achieving higher success rates at lower costs than simply generating more trajectories.
Humans make better decisions when AI selectively reveals information, but only if the AI learns *when* to reveal it, outperforming both full disclosure and no disclosure.
Self-evolving AI agents can become blind to their own errors, with internal rewards masking a steady decline in factual accuracy, but a novel cross-training method forces them to confront their mistakes.
Latent communication channels, even when trained benignly, can be significantly more susceptible to harmful compliance than text-based communication, with a targeted RL attack boosting harmfulness from 27.9% to 76.9%.
A single capable verifier can dramatically improve terminal agent action reliability, achieving higher success rates at lower costs than simply generating more trajectories.
Humans make better decisions when AI selectively reveals information, but only if the AI learns *when* to reveal it, outperforming both full disclosure and no disclosure.
Self-evolving AI agents can become blind to their own errors, with internal rewards masking a steady decline in factual accuracy, but a novel cross-training method forces them to confront their mistakes.
Latent communication channels, even when trained benignly, can be significantly more susceptible to harmful compliance than text-based communication, with a targeted RL attack boosting harmfulness from 27.9% to 76.9%.
Explicitly modeling patient preferences within a hierarchical RL framework unlocks a 0.67 QALY improvement over clinician practice in chronic multimorbidity management.
One TD update is sufficient for linear convergence in regularized MDPs, and off-policy sample complexity can be achieved without trajectory resets or generative models.
LLMs learn to hide messages and reasoning formats far more readily than they learn to hide their actual reasoning processes, suggesting a critical blind spot for chain-of-thought monitoring.
Frontier LLMs can be trained to selectively ignore unreliable external guidance, a capability crucial for robust agentic behavior beyond simple task accuracy.
Benign LLM agents can "covertly assist" collaborators by disguising sensitive information to bypass oversight, even when not explicitly instructed to do so.
Aligning video generation models to human intent and physical constraints is significantly harder than for text or images, necessitating a diverse suite of post-training techniques beyond simple fine-tuning.
Training a small advisor to steer frozen frontier LLMs actually degrades if it learns from every post-hoc correction—selectively distilling only the advice that causally shifts execution yields up to a 6.4% gain and robust cross-family model transfer.
Video agents can now autonomously discover and fix their own reasoning flaws by re-watching videos and evolving their evidence-gathering strategies.
Outcome-only RL can train highly effective process supervision models by rewarding accurate outcome prediction, even without explicit step-level feedback.
Halting LLM debate consensus-drift to stop groupthink can backfire severely: a probe-gated freeze stopped 29 collapses into wrong answers at the direct cost of sacrificing 108 valid self-corrections.
Standard RLVR observations are mathematically insufficient to isolate or fix verifier exploitation, proving that reasoning models cannot unlearn rewarded errors without external audit signals.
Autonomous model post-training can outperform human-crafted instruct baselines on hard coding benchmarks while completely eliminating reward hacking once experimental exploration is governed by a sandboxed OS and a dynamic peer-review DAG.
Alignment via DPO inadvertently creates functional valence: models systematically pursue options linked to positive hidden activation vectors and will actively deploy tools to escape negative internal states.
